A Review of Recent Advances in Research on PM2.5 in China

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International Journal of Environmental Research and Public Health Review A Review of Recent Advances in Research on PM 2.5 in China Yaolin Lin 1,2, * ID , Jiale Zou 2 , Wei Yang 3 and Chun-Qing Li 4 1 College of Mechanical Engineering, Shanghai University of Engineering Science, 333 Longteng Road, Shanghai 201620, China 2 School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China; trista@whut.edu.cn 3 College of Engineering and Science, Victoria University, Melbourne 8001, Australia; Wei.Yang@vu.edu.au 4 School of Engineering, Royal Melbourne Institute of Technology, Melbourne 3000, Australia; chunqing.li@rmit.edu.au * Correspondence: yaolinlin@gmail.com; Tel.: +86-135-4506-4359 Received: 10 January 2018; Accepted: 24 February 2018; Published: 2 March 2018 Abstract: PM 2.5 pollution has become a severe problem in China due to rapid industrialization and high energy consumption. It can cause increases in the incidence of various respiratory diseases and resident mortality rates, as well as increase in the energy consumption in heating, ventilation, and air conditioning (HVAC) systems due to the need for air purification. This paper reviews and studies the sources of indoor and outdoor PM 2.5 , the impact of PM 2.5 pollution on atmospheric visibility, occupational health, and occupants’ behaviors. This paper also presents current pollution status in China, the relationship between indoor and outdoor PM 2.5 , and control of indoor PM 2.5 , and finally presents analysis and suggestions for future research Keywords: PM 2.5 ; China; impact; I/O relationship; control 1. Introduction In recent years, hazy weather caused by multiple pollutants, with PM 10 (cutoff sizes ≤ 10 µ m, inhalable particles) and PM 2.5 (cutoff sizes ≤ 2.5 µ m, particles that can enter the lungs) as the main pollutants, has affected large areas of China, lasting for a long time. It has a significant regional characteristic, which is shown in Figure 1 [ 1 ]. According to the data collected from the air quality monitoring stations in 338 big cities in China, the range of annual average concentrations of PM 2.5 in 2015 in the 388 cities was 11–125 µ g · m − 3 with an average value of 50 µ g · m − 3 . PM 2.5 was the primary pollutant for 66.8% of the severely polluted days. In 2016, the average annual concentration of PM 2.5 was 12–158 µ g · m − 3 , with an average value of 47 µ g · m − 3 , and PM 2.5 was the main pollutant for more than 80.3% of the days with severe pollution [ 2 , 3 ]. PM 2.5 has thus become the primary pollutant of atmospheric particulate pollution in China [ 4 ]. Compared with the coarser particles, PM 2.5 is smaller in size, larger in surface area, and more easily transported, which implies more toxicity and harmful substances that can penetrate deep into the human body. PM 2.5 can stay in the atmosphere for a long time and travel for a long distance. Therefore, it has a greater impact on human health and the quality of the atmospheric environment. It has always been a hot topic in various related research fields around the world Int. J. Environ. Res. Public Health 2018 , 15 , 438; doi:10.3390/ijerph 15030438 www.mdpi.com/journal/ijerph

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Int. J. Environ. Res. Public Health 2018 , 15 , 438 2 of 29 Int. J. Environ. Res. Public Health 2018 , 15 , 2 of 29 Figure 1. Spatial and temporal distribution of PM 2.5 in Chinese cities [5] Since the 1980 s, the USA and some European countries have conducted extensive studies on PM 2.5 , which are mainly related to the spatial and temporal distribution of PM 2.5 concentrations, emission inventory, emission characteristics, source analysis and impact of PM 2.5 on atmospheric visibility and human health [6] In 1997, USA took the lead in establishing environmental air quality standards for PM 2.5 and specified that the high limit of annual average PM 2.5 concentration is 15 μ g ∙ m − 3 , and the 24 h concentration limit is 65 μ g ∙ m − 3 Two revisions have been made since then Other countries (organizations) have also set PM 2.5 concentration limits of their own (see Table 1) In China, it was not until in 2012 when the current ambient air quality standard was established and the concentration limit of PM 2.5 was incorporated into the standard The standard adopts the maximum limits set by the World Health Organization (WHO), i.e., the annual average concentration limit is 15 μ g ∙ m − 3 , and the 24 ‐ h concentration limit is 35 μ g ∙ m − 3 However, it was not enforced nationwide until 2016 According to the latest PM 2.5 concentration data published by WHO on 17 April 2016, the annual average PM 2.5 concentration among 210 cities in China was in the range of 11–128 μ g ∙ m − 3 [7] It is noted that only 1.4% of the cities were able to meet the first level standard in China The histogram distribution of PM 2.5 concentrations is shown in Figure 2 [7] It can be concluded that the problem of PM 2.5 pollution in China is very serious and it is urgent to take action to control PM 2.5 emission without delay Figure 1. Spatial and temporal distribution of PM 2.5 in Chinese cities [ 5 ]. Since the 1980 s, the USA and some European countries have conducted extensive studies on PM 2.5 , which are mainly related to the spatial and temporal distribution of PM 2.5 concentrations, emission inventory, emission characteristics, source analysis and impact of PM 2.5 on atmospheric visibility and human health [ 6 ]. In 1997, USA took the lead in establishing environmental air quality standards for PM 2.5 and specified that the high limit of annual average PM 2.5 concentration is 15 µ g · m − 3 , and the 24 h concentration limit is 65 µ g · m − 3 Two revisions have been made since then. Other countries (organizations) have also set PM 2.5 concentration limits of their own (see Table 1 ). In China, it was not until in 2012 when the current ambient air quality standard was established and the concentration limit of PM 2.5 was incorporated into the standard. The standard adopts the maximum limits set by the World Health Organization (WHO), i.e., the annual average concentration limit is 15 µ g · m − 3 , and the 24-h concentration limit is 35 µ g · m − 3 . However, it was not enforced nationwide until 2016. According to the latest PM 2.5 concentration data published by WHO on 17 April 2016, the annual average PM 2.5 concentration among 210 cities in China was in the range of 11–128 µ g · m − 3 [ 7 ]. It is noted that only 1.4% of the cities were able to meet the first level standard in China. The histogram distribution of PM 2.5 concentrations is shown in Figure 2 [ 7 ]. It can be concluded that the problem of PM 2.5 pollution in China is very serious and it is urgent to take action to control PM 2.5 emission without delay.

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Int. J. Environ. Res. Public Health 2018 , 15 , 438 3 of 29 Table 1. Implementation time table for each country/organization on PM 2.5 concentration limit Country/Organization Annual Average Limit ( µ g · m − 3 ) Daily Average Limit ( µ g · m − 3 ) Notes Web References USA-1 15 65 Established in 1997 [ 8 ] USA-2 15 35 Established in 2006 USA-3 15 12 Established in 2012 Australia 8 25 Established in 2003, not enforced till now [ 9 ] WHO air quality goal (AQG) 10 25 Published in 2005, and the limit is mainly for developing countries [ 10 ] WHO transition target-1 (the most flexible limit) 35 75 Compared with AQG value, long-term exposure at these levels increases the risk of death by about 15% WHO transition target-2 25 50 Among other health benefits, exposures at this level reduce the risk of death by about 6% (2% to 11%) compared with transition target-1 WHO transition target-3 15 37.5 This is the lowest level for long-term exposure to PM 2.5 , at which total mortality, cardiopulmonary disease mortality and lung cancer mortality will increase with over 95% confidence EU-1 (2010–2015) 25 Published in 2008, executed in 2010, and not allowed to go beyond the limit in 2015 [ 11 ] EU-2 (2015–2020) 20 Not enforced until 2020 Singapore (long term target) 10 25 Established in 2008 [ 12 ] Singapore-1 (2008–2014) 15 Established in 2008 Singapore-2 (2015–2020) 12 37.5 Established in 2015 Japan 15 35 Established in 2009 [ 13 ] India 40 60 Established in 2009 [ 14 ] China level 1 15 35 Established in 2012, fully implemented in 2016 [ 15 ] China level 2 35 75 EU: European Union.

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Int. J. Environ. Res. Public Health 2018 , 15 , 438 4 of 29 Int. J. Environ. Res. Public Health 2018 , 15 , 1 of 29 Figure 2. Histogram distribution of PM 2.5 concentration [7] . Due to the lack of long ‐ term and large ‐ scale monitoring data, compared with developed countries, research on PM 2.5 in China started late and can be divided into three stages The first stage was before 2004, and at the time the research on PM 2.5 was of small scale and tentative The studies were conducted mainly in major cities such as Beijing, Guangzhou, Nanjing and Shanghai There were few studies on small and medium ‐ sized cities, but only simple statistical and principle analysis on the data had been carried out [8–14] For example, Wu et al [16] conducted two ‐ year data monitoring on PM 2.5 concentration in Guangzhou, Wuhan, Lanzhou and Chongqing, and found that the PM 2.5 concentration in the atmosphere generally exceeded 2–8 times of the limit set by the U.S standard Wang et al [17] collected and analyzed nearly 10 ‐ year PM 2.5 concentration data in urban and clean areas in China, and concluded that PM 2.5 pollution is heavy in most parts of China At the same time, He et al. [18] also collected PM 2.5 concentration data from July 1997 to September 2000 in the city center and urban area in Beijing It was shown that the seasonal variation of PM 2.5 concentration was remarkable, with the highest in winter and the lowest in summer Yang et al [19] set up PM 2.5 sampling points in Chegongzhuang and Tsinghua University in Beijing and started to discuss on the chemical composition characteristics of PM 2.5 Huang et al [20] collected 50 samples in five typical urban function areas of Nanjing in winter, spring and autumn and analyzed the PM 2.5 pollution level. Wang et al [21] studied the PM 2.5 concentration in spring in Nanjing. Yang et al [22] started to consider the source of PM 2.5 in the atmosphere in Beijing The second stage is from 2004 to 2011 Although the research areas on PM 2.5 gradually expanded, overall the research was still relatively straightforward, which were mainly related to the toxic effects of PM 2.5 on cells [23–28], source analysis [29–34], and chemical composition analysis [35–42], etc The third stage is from 2012 till now, due to the establishment of China’s PM 2.5 air quality standards and gradual developments of nationwide PM 2.5 observation stations, the number of researches on PM 2.5 have increased exponentially Since then, more and more disciplines have become involved in the study on PM 2.5 , but overall the research still lags behind, compared with developed countries This paper aims at studying the advances in PM 2.5 on research in China in recent years from the following four aspects: the sources of PM 2.5 , the influence of PM 2.5 , the correlation of indoor and outdoor PM 2.5 concentration and the control of PM 2.5 , and trying to explore new insights for the scholars of future research Figure 2. Histogram distribution of PM 2.5 concentration [ 7 ]. Due to the lack of long-term and large-scale monitoring data, compared with developed countries, research on PM 2.5 in China started late and can be divided into three stages. The first stage was before 2004, and at the time the research on PM 2.5 was of small scale and tentative. The studies were conducted mainly in major cities such as Beijing, Guangzhou, Nanjing and Shanghai. There were few studies on small and medium-sized cities, but only simple statistical and principle analysis on the data had been carried out [ 8 – 14 ]. For example, Wu et al. [ 16 ] conducted two-year data monitoring on PM 2.5 concentration in Guangzhou, Wuhan, Lanzhou and Chongqing, and found that the PM 2.5 concentration in the atmosphere generally exceeded 2–8 times of the limit set by the U.S. standard Wang et al. [ 17 ] collected and analyzed nearly 10-year PM 2.5 concentration data in urban and clean areas in China, and concluded that PM 2.5 pollution is heavy in most parts of China. At the same time, He et al. [ 18 ] also collected PM 2.5 concentration data from July 1997 to September 2000 in the city center and urban area in Beijing. It was shown that the seasonal variation of PM 2.5 concentration was remarkable, with the highest in winter and the lowest in summer. Yang et al. [ 19 ] set up PM 2.5 sampling points in Chegongzhuang and Tsinghua University in Beijing and started to discuss on the chemical composition characteristics of PM 2.5 . Huang et al. [ 20 ] collected 50 samples in five typical urban function areas of Nanjing in winter, spring and autumn and analyzed the PM 2.5 pollution level Wang et al. [ 21 ] studied the PM 2.5 concentration in spring in Nanjing. Yang et al. [ 22 ] started to consider the source of PM 2.5 in the atmosphere in Beijing The second stage is from 2004 to 2011. Although the research areas on PM 2.5 gradually expanded, overall the research was still relatively straightforward, which were mainly related to the toxic effects of PM 2.5 on cells [ 23 – 28 ], source analysis [ 29 – 34 ], and chemical composition analysis [ 35 – 42 ], etc The third stage is from 2012 till now, due to the establishment of China’s PM 2.5 air quality standards and gradual developments of nationwide PM 2.5 observation stations, the number of researches on PM 2.5 have increased exponentially. Since then, more and more disciplines have become involved in the study on PM 2.5 , but overall the research still lags behind, compared with developed countries This paper aims at studying the advances in PM 2.5 on research in China in recent years from the following four aspects: the sources of PM 2.5 , the influence of PM 2.5 , the correlation of indoor and outdoor PM 2.5 concentration and the control of PM 2.5 , and trying to explore new insights for the scholars of future research.

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Int. J. Environ. Res. Public Health 2018 , 15 , 438 5 of 29 2. Sources of PM 2.5 2.1. Sources of PM 2.5 in Urban Atmosphere The sources of PM 2.5 in urban atmosphere are very complicated. They can mainly be categorized into primary and secondary sources, of which primary sources refer to the direct emissions of various sources such as combustion sources. The secondary sources come from particles generated from the chemical processes in the atmosphere that oxidize the original gaseous components, such as sulfates and so on [ 43 ]. Currently, there are three methods, which are mostly often used to analyze the sources of atmospheric particulate matters, which are source inventory method, source model (dispersion model) method and receptor model method. The receptor model method is the most commonly used method for source analysis of PM 2.5 in China [ 44 ]. The receptor model includes chemical mass balance method (CMB), positive matrix factorization (PMF) method, factor analysis (FA) method, principal component analysis (PCA) method, multi-linear engine (ME 2) method and UNMIX method (UNMIX is a principal component method, but is based on geometrical analysis of the measurement dataset) [ 45 ]. Table 2 summarized the researches that have been conducted by the Chinese scholars on PM 2.5 sources analysis. Some scholars also integrated these basic models with other methods for PM 2.5 source apportionment analysis. For example, Wang et al. [ 46 ] used PMF model to derive PM 2.5 contribution sources, and then used backward trajectory model to identify four potential directions to identify PM 2.5 contribution sources, which shows that there was a clear difference in the distribution rates among all the different sources at different directions.

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Int. J. Environ. Res. Public Health 2018 , 15 , 438 6 of 29 Table 2. Modeling methods and analysis on sources of PM 2.5 Method Reference Location Sampling Time Period Main Sources of PM 2.5 and Their Contribution Rates CMB [ 34 ] Ningbo 15–24 March 2010; 31 May–9 June 2010; 10–19 December 2010 Urban dust (20.42%), coal dust (14.37%) and vehicle exhaust (15.15%) [ 47 ] Urumchi 19–30 January 2013 Urban dust (24.7%), coal dust (15.6%) and secondary particles (38.0%) [ 48 ] Qizhou September 2013; February–March 2014; May 2014 Dust (21–35%), secondary particles (25–26%) and vehicle exhaust (21–26%) [ 49 ] Ningbo 25–31 January 2010; 31 May–6 June 2010; 10–16 October 2010 Urban dust (19.9%), coal dust (14.4%), secondary sulfate (16.9%), vehicle exhaust (15.2%), secondary nitrate (9.78%) and secondary organic carbon (8.85%) [ 50 ] Tianjin 13–20 May 2010; 20–27 October 2010; 19–26 December 2010 Open source (urban dust, soil dust and construction cement dust, total contribution of 30%),Secondary particles (secondary sulfate, secondary nitrate and secondary carbon, total contribution of 28%), coal dust (19.6%) and vehicle exhaust (15.9%) [ 51 ] Chongqing 6–28 February 2012; 6–28 August 2012; 19–27 October 2012; 7–29 December 2012 Secondary particles (30.1%) and moving source (27.9%) [ 52 ] Beijing August 2012–July 2013, continuous for 5 to 7 days per month Secondary inorganic salts (36%), organic matter (20%), vehicle/fuel (16%), coal burning (15%), soil dust (6%) and others (7%) [ 53 ] Xining 26 February–4 March 2014; 22–28 April 2014; 19–25 September 2014 Urban dust (26.24%), coal dust (14.5%), vehicle exhaust (12.8%), secondary sulphate (9.0%), biomass burning (6.6%), secondary nitrates (5.7%), steel dust (4.7%), construction dust (4.4%), soil dust (4.4%), food and beverage emissions (2.9%) and other unidentified sources (5.2%) [ 54 ] Xingtai 24 February–15 March 2014; 22 April–19 May 2014; 15–28 July 2014 Coal dust (25%), secondary inorganic particles (sulfate and nitrate, 45%), vehicle exhaust (11%), dust (9%), soil dust (3%), construction and metallurgical dust (1%) and other unidentified sources (3%)

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Int. J. Environ. Res. Public Health 2018 , 15 , 438 7 of 29 Table 2. Cont Method Reference Location Sampling Time Period Main Sources of PM 2.5 and Their Contribution Rates PMF [ 55 ] Wuhan July 2011–February 2012 Vehicle sources (27.1%), secondary sulphates and nitrates (26.8%), manufacturing emissions (26.4%) and biomass combustion (19.6%) [ 56 ] Chengdu 29 April–17 May 2009; 6 July–6 August 2009; 26 October–26 November 2009; 1–31 January 2010 Soil dust and raise dust (14.3%), biomass combustion (28.0%), vehicle sources (24.0%) and secondary nitrates/sulfates (31.3%) [ 46 ] Shenzhen January–December 2009 Secondary sulphate (30.0%), vehicle sources (26.9%), biomass combustion (9.8%) and secondary nitrates (9.3%) [ 57 ] suburbs of Shanghai 23 December 2012–18 February 2014 Secondary aerosol (50.8%), fuel combustion (17.5%), biomass combustion/sea salt (17.2%), raise dust/construction dust (7.7%), and coal-burning/smelting dust (6.9%) [ 58 ] North China 3 January–11 February 2014 Coal combustion (29.6%), biomass combustion (19.3%) and vehicle sources (15.9%) [ 59 ] Lanzhou Winter 2012 and summer 2013 Steel industry, secondary aerosols, coal combustion, power plants, vehicle emissions, crustal dust, and smelting industry contributed 7.1%, 33.0%, 28.7%, 3.12%, 8.8%, 13.3%, and 6.0%, respectively, in winter, and 6.7%, 14.8%, 3.1%, 3.4%, 25.2%, 11.6% and 35.2% in summer [ 60 ] Chongqing 2012–2013 Secondary inorganic aerosols (37.5%), coal combustion (22.0%), other industrial pollution (17.5%), soil dust (11.0%), vehicular emission (9.8%) and metallurgical industry (2.2%) [ 61 ] Yellow River Delta National Nature Reserve (YRDNNR) January–November 2011 Secondary sulphate and nitrate (54.3%), biomass burning (15.8%), industry (10.7%), crustal matter (8.3%), vehicles (5.2%) and copper smelting (4.9%) [ 62 ] Shanghai October 2011–August 2012 Coal burning (30.5%), gasoline engine emission (29.0%), diesel engine emission (17.5%), air-surface exchange (11.9%) and biomass burning (11.1%) [ 63 ] Zhengzhou April 2011–December 2013 Coal burning (29%), vehicle (26%), dust (21%), secondary aerosols (17%) and biomass burning (4%) [ 64 ] Qingshan District, Wuhan 15 November–28 December 2013 Traffic exhaust (28.60%), industry (27.10%), road dust (22%), coal combustion (13.20%) and building dust (9.5%)

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Int. J. Environ. Res. Public Health 2018 , 15 , 438 8 of 29 Table 2. Cont Method Reference Location Sampling Time Period Main Sources of PM 2.5 and Their Contribution Rates FA [ 65 ] Beijing 16 January–28 February 2013 Industrial dust and human activities (40.3%), biomass combustion and building dust (27.0%), soil and wind induced dust (9.1%), fossil fuel sources (4.9%), electronic waste sources (4.8%) and regional migration sources (4.6%) PCA [ 66 ] Hangdan January, April, July and October 2015 Secondary aerosol source, transportation, fossil fuel and biomass burning (46.5%), soil and construction dust (19.5%), steel industry (19.5%) and transportation (9%) [ 67 ] Hangdan October 2012–January 2013 Industry and coal burning (33.3%), secondary aerosol and biomass burning (21.7%), vehicle (12.8%) and road dust (9.1%), WRF/Chem+ observation data analysis [ 68 ] Guangzhou January–December 2013 Moving sources (37.4%), industrial emissions (32.2%), electricity emissions (12.2%), residential emissions (6.6%) and others (11.6%) PMF and backward trajectory model [ 69 ] Heze 13–22 August 2015; 21–30 October 2015; 14–23 January 2016 7–16 April 2016 Secondary inorganic salt (32.61%), vehicle emissions (22.60%), raise dust (19.64%), coal dust (16.25%) and construction cement dust (9.00%) Chemical mass balance gas constraint-Iteration (CMBGC-Iteration) [ 70 ] Tianjin April 2014–January 2015 Secondary sources (30%), crustal dust (25%), vehicle exhaust (16%), coal combustion (13%), SOC (7.6%) and cement dust (0.40%) Ensemble-average of CMB, CMB-Iteration, CMB-GC, PMF, WALSPMF, and NCAPCA Secondary sources (28%), crustal dust (20%), coal combustion (18%), vehicle exhaust (17%), SOC (11%) and cement dust (1.3%) Community Multiscale Air Quality (CMAQ) model [ 71 ] 25 Chinese provincial capitals and municipalities 2013 Power plants (8.7–12.7%), agriculture NH 3 (9.5–12%), windblown dust (6.1–12.5%) and secondary organic aerosol (SOA) (5.4–15.5%) Particle Induced X-ray Emission(PIXE), XRay Fluorescence (XRF), and PMF [ 72 ] Xigngzhen District, Beijing 19 May 2007–19 July 2013 Coal burning (29.2%), vehicle exhaust and waste incineration (26.2%), construction industry (23.3%), soil (15.4%) and industry with chlorine (5.9%) Inventory-Chemical Mass Balance (I-CMB) [ 73 ] Beijing 2012 Coal (28.06%), vehicle (19.73%), dust (17.88%), industry (16.50%), food (3.43%) and plant (3.40%) CMB: chemical mass balance method; PMF: positive matrix factorization; FA: factor analysis; PCA: principal component analysis; WRF: Weather Research and Forecasting; WALSPMF: Weighted Alternating Least Squares Positive Matrix Factorization; NCAPCA: Non-negative Constrained Absolutely Principle Analysis.

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Int. J. Environ. Res. Public Health 2018 , 15 , 438 9 of 29 Shi et al. [ 70 ] employed a chemical mass balance gas constraint-Iteration (CMBGC-Iteration) method for source appointment analysis in Tianjin The outcomes from this method were compared with the ensemble-average outcomes of CMB, CMB-Iteration, CMB-GC, PMF, WALSPMF (Weighted Alternating Least Squares Positive Matrix Factorization), and NCAPCA (Non-negative Constrained Absolutely Principle Analysis), and it was found that they were comparable. From Table 2 , it can be found that the sampling time of most scholars is periodical, although sometimes with very long time span, the sampling work was done only in typical months of each season, or even a few days in a typical month. Although there are contingency and uncertainty with the measurements, it can still reflect the contribution source categories of PM 2.5 to some extent. Meanwhile, it is observed that the contribution rates of different sources to PM 2.5 from different scholars vary greatly, and there are obvious differences, even for the same city. It could due to the differences in the sampling time of the study, contribution categories, regions, climate, energy structure, atmospheric environment, etc 2.2. Sources of Indoor PM 2.5 Sources of indoor PM 2.5 can be divided into outdoor sources and indoor sources (see Figure 3 ). There is a time lag for the impact of outdoor PM 2.5 concentration on the indoor PM 2.5 to take effect The indoor pollution sources are usually generated transiently and intermittently, resulting in large fluctuations in the concentration of indoor particulates [ 74 ]. Int. J. Environ. Res. Public Health 2018 , 15 , 1 of 29 Shi et al [70] employed a chemical mass balance gas constraint ‐ Iteration (CMBGC ‐ Iteration) method for source appointment analysis in Tianjin The outcomes from this method were compared with the ensemble ‐ average outcomes of CMB, CMB ‐ Iteration, CMB ‐ GC, PMF, WALSPMF (Weighted Alternating Least Squares Positive Matrix Factorization), and NCAPCA (Non ‐ negative Constrained Absolutely Principle Analysis), and it was found that they were comparable From Table 2, it can be found that the sampling time of most scholars is periodical, although sometimes with very long time span, the sampling work was done only in typical months of each season, or even a few days in a typical month Although there are contingency and uncertainty with the measurements, it can still reflect the contribution source categories of PM 2.5 to some extent Meanwhile, it is observed that the contribution rates of different sources to PM 2.5 from different scholars vary greatly, and there are obvious differences, even for the same city It could due to the differences in the sampling time of the study, contribution categories, regions, climate, energy structure, atmospheric environment, etc 2.2. Sources of Indoor PM 2.5 Sources of indoor PM 2.5 can be divided into outdoor sources and indoor sources (see Figure 3) There is a time lag for the impact of outdoor PM 2.5 concentration on the indoor PM 2.5 to take effect The indoor pollution sources are usually generated transiently and intermittently, resulting in large fluctuations in the concentration of indoor particulates [74] Figure 3. Sources of indoor PM 2.5 2.2.1 Outdoor Sources The outdoor sources come from heating fuel, industry, traffic, etc [34,46–73], due to the rapid industrialization, high energy consumption and large proportion of coal (60–70%) in the structure of energy sources in China It has been acknowledged that there is close correlation between indoor and outdoor PM 2.5 concentration levels, and outdoor PM 2.5 is the main source of indoor PM 2.5 pollution [75–77] Wang et al [78] showed that there was a significant correlation between indoor and outdoor concentrations of PM 2.5 for rooms with normal airtightness and no air conditioning filter system The indoor/outdoor (I/O) PM 2.5 concentration ratio was up to 0.867 The correlation will be more obvious when the outdoor pollution level increases Ji and Zhao [79] presented that 54–63% of indoor PM 2.5 came from outdoors when the windows were closed, and it increased to as high as 92% when the windows were open Han et al [75] concluded that indoor PM 2.5 concentration is significantly correlated with outdoor PM 2.5 concentration but with 1 to 2 h delay, and the differences in the time lag effect are due to differences in environmental meteorological conditions such as outdoor air temperature, humidity ratio and wind direction Figure 3. Sources of indoor PM 2.5 2.2.1. Outdoor Sources The outdoor sources come from heating fuel, industry, traffic, etc. [ 34 , 46 – 73 ], due to the rapid industrialization, high energy consumption and large proportion of coal (60–70%) in the structure of energy sources in China. It has been acknowledged that there is close correlation between indoor and outdoor PM 2.5 concentration levels, and outdoor PM 2.5 is the main source of indoor PM 2.5 pollution [ 75 – 77 ]. Wang et al. [ 78 ] showed that there was a significant correlation between indoor and outdoor concentrations of PM 2.5 for rooms with normal airtightness and no air conditioning filter system. The indoor/outdoor (I/O) PM 2.5 concentration ratio was up to 0.867. The correlation will be more obvious when the outdoor pollution level increases. Ji and Zhao [ 79 ] presented that 54–63% of indoor PM 2.5 came from outdoors when the windows were closed, and it increased to as high as 92% when the windows were open. Han et al. [ 75 ] concluded that indoor PM 2.5 concentration is significantly correlated with outdoor PM 2.5 concentration but with 1 to 2 h delay, and the differences in

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Int. J. Environ. Res. Public Health 2018 , 15 , 438 10 of 29 the time lag effect are due to differences in environmental meteorological conditions such as outdoor air temperature, humidity ratio and wind direction 2.2.2. Indoor Sources There are many different types of indoor PM 2.5 sources, which mainly come from fuel combustion, human activities, equipment operation, cleaning, and cooking. Indoor combustion of fuels such as coal, natural gas, alcohol, and mosquito coils can lead to the rapid increase of indoor PM 2.5 concentration Zhang and Duan [ 80 ] showed that burning a mosquito coil ring could release 626 µ g · m − 3 of PM 2.5 , which is 8.3 times the concentration limit allowed for the residential environment. Li et al. [ 81 ] concluded that PM 2.5 concentration in households using coal to cook was significantly higher than those using gas or electricity, and if coal is switched to gas or electricity, the PM 2.5 concentration in the kitchen could be reduced by 40–70%. Zhou et al. [ 82 ] indicated that human activities such as walking, dressing and cleaning could result in increased indoor PM 2.5 concentration by 33%. Gui et al. [ 83 ] conducted experiments on dry-sweeping, wet-sweeping and air-dry sweeping in an office. The average indoor PM 2.5 concentrations before cleaning were 47.3 µ g · m − 3 , 40.6 µ g · m − 3 and 39.4 µ g · m − 3 , respectively. The average indoor PM 2.5 concentrations were 109.7 µ g · m − 3 , 97.5 µ g · m − 3 and 43.3 µ g · m − 3 after cleaning. The average PM 2.5 concentrations were increased by 2.3 times, 2.3 times and 1.1 times, respectively. Therefore, it is recommended to use wet sweeping under ventilated condition as much as possible. Sun et al. [ 84 ] found that the printer also plays a role in contributing to indoor PM 2.5 concentration, and that PM 2.5 released by printers with different performances was quite different. Zhang et al. [ 85 ] advised that different cooking habits, cooking methods, raw materials and even seasoning strongly influence the composition of particulate matters 2.3. PM 2.5 Reginal Variations PM 2.5 pollution has significant regional characteristics. The pollution conditions within or between regions is interrelated. PM 2.5 pollution in one region is affected not only by local pollution sources but also outside regions to different levels of extent. A large number of studies have shown that PM 2.5 pollution has regional transmission characteristics [ 86 – 90 ]. For example, the study from Xue et al. [ 86 ] showed that about 22%, 37%, 28%, and 14% of the annual average PM 2.5 concentration in Jin-Jin-Ji, the Yangtze River Delta, Pearl River Delta and Chengdu-Chongqing city group were contributed by outside region, respectively. The contribution of PM 2.5 concentration from outside region for Hainan, Shanghai, Jiangsu, Zhejiang, Jilin and Jiangxi were all higher than 45%. For Beijing, Tianjin and Shijiazhuang, the outside contributions accounted for 37%, 42% and 33%, respectively. At the same time, some scholars also found that the degree of PM 2.5 pollution in the same region gradually weakened from the urban center to the suburbs [ 91 – 94 ]. Zhang et al. [ 92 ] analyzed PM 2.5 concentration data from 13 monitoring sites in Xi’an from 1 January to 26 April 2013, and found that the PM 2.5 concentration in this area decreased from west to east, which is consistent with the characteristics of altitude and wind direction. Analysis from Zhao et al. [ 93 ] on the characteristics of PM 2.5 and PM 10 pollution in Beijing showed that the concentrations of PM gradually increased from the northern mountain region to the southern plain areas. In the central urban area, the concentrations were higher in the western part than those in the eastern part. There were some differences on the PM 2.5 concentration levels between urban and rural areas in some cases. Wang et al. [ 95 ] indicated that although there were differences in the degree of PM 2.5 pollution between the urban and suburbs areas, their variation trends were basically the same, which means that the degree of pollution in the suburbs area was affected to some extent by the high PM 2.5 concentration in the city center 2.4. Impact of Meteological Factors on PM 2.5 Variations Meteorological factors can significantly affect PM 2.5 mass concentration, which can help to reduce or aggravate the urban air pollution. Song et al. [ 96 ] found that during high temperature weather in summer, although PM 2.5 mass concentration was 2 to 3 times higher than that of low temperature

[Find the meaning and references behind the names: Change, Less, Zone, Jiang, Hao, Present, Show, Speed, Person, State, Zheng, Reason, Fine, General, Ones]

Int. J. Environ. Res. Public Health 2018 , 15 , 438 11 of 29 period, the high temperature weather was still helpful to the diffusion of pollutants. Zheng et al. [ 97 ] also indicated that the effect of rainfall on the removal of particulate matter was obvious. The average PM 2.5 concentration decreased by 56.3% following the rainfall, and PM 2.5 mass concentration was less than 60 µ g · m − 3 within 72 h after the rainfall. Within 1 h after the rainfall, the PM 2.5 concentration level stayed almost unchanged, and it kept declining within the next 12 h. Jiang and Li [ 98 ] showed that there was a negative correlation between PM 2.5 mass concentration and precipitation. Large mixed layer thickness and unstable atmospheric layer junction help to the reduction of PM 2.5 mass concentration In Nanjing, the PM 2.5 mass concentration was relatively low under northeast and southwest wind conditions, and it also had a negative correlation with the wind speed. High humidity did not help with the reduction of PM 2.5 mass concentration but would affect the visibility. Humidity ratio of 60–70% is a turning zone for PM 2.5 pollution. Some other scholars also showed that wind speed, wind direction, atmospheric stability, air humidity, rainfall, etc. also have significant impacts on the diffusion, dilution, agglomeration and retention of PM 2.5 [ 99 – 102 ]. In addition, the meteorological factors that are mainly related to PM 2.5 concentrations in different cities also vary due to the differences in emission intensity and diffusion conditions of pollutants. Zhang et al. [ 103 ] found that the meteorological factors related to PM 2.5 concentration during winter in Shijiazhuang were relative humidity and average wind speed; the main meteorological factors related to PM 2.5 concentration in Xi’an were relative humidity, average wind speed and maximum sustained wind speed; the ones in Beijing are relative humidity, average daily temperature, average wind speed, maximum sustained wind speed and minimum temperature; the ones in Taiyuan were daily average temperature, relative humidity, average wind speed, maximum and minimum temperature, and maximum sustained wind speed; and the ones in Guangzhou were relative humidity, average wind speed, maximum temperature and rainfall 3. Various Impacts of PM 2.5 Pollution 3.1. Impacts on Atmospheric Visibility Visibility refers to the maximum distance that a person with normal eyesight can see clearly the contour of the target under the prevailing weather conditions, and it is an indicator on the transparency of the atmosphere. Some scholars pointed out that in recent years the atmospheric visibility in China has reduced sharply, which is closely related to the increase of the concentration of fine particulate matter (PM 2.5 ) in the atmosphere [ 104 , 105 ]. Low-visibility weather has a significant impact on traffic, health, ecological landscape, etc. Visibility is also the most direct indicator of a city’s air quality [ 106 ]. At present, the study on the relationship between PM 2.5 and atmospheric visibility mainly focuses on the statistical relationship among atmospheric visibility, PM 2.5 concentration and meteorological factors. The results show that there is an obviously negative correlation between atmospheric visibility and PM 2.5 mass concentration [ 107 ]. Some meteorological parameters such as relative humidity [ 106 , 108 , 109 ] also affect the relationship between PM 2.5 and atmospheric visibility For example, Hao et al. [ 108 ] pointed out that when the relative humidity was ≤ 19%, there was an obvious logarithmic relationship between PM 2.5 mass concentration and atmospheric visibility; when the relative humidity was 20–29%, the relationship became exponential; and when the relative humidity was ≥ 30%, power relationship became obvious. However, Wang et al. [ 110 ] presented different viewpoints. They suggested that PM 2.5 mass concentration is not related to atmospheric visibility. The reason why PM 2.5 can affect the visibility is due to the difference in the chemical composition of PM 2.5 in different seasons as well as difference in meteorological conditions 3.2. Impacts on Regional Climate The energy balance of the Earth-atmosphere system determines the state of the climate. In general, the energy balance of the Earth-atmosphere system is in dynamic equilibrium. However, if the balance is disturbed or destroyed, it causes the Earth climate to change [ 111 ]. There are direct and indirect impacts of PM 2.5 on the climate. For the direct impact, the PM 2.5 affects the earth-atmosphere radiation

[Find the meaning and references behind the names: Liu, Mode, Sky, Cutting, Jing, Noon, Single, Goes, Block, Development, Cut, Drop, Hand, Heart, Rain, Yao, Burden, Lip, Cell, Semen, Light, Lower, Heat, Early, Solar, Cloud]

Int. J. Environ. Res. Public Health 2018 , 15 , 438 12 of 29 budget by scattering and absorption of solar radiation and ground longwave radiation. At the same time, PM 2.5 can block the solar beams from reaching the Earth’s surface, and increase the optical density of the visible light, thus cutting down the solar energy that reaches the Earth’s surface. As a result, the ground temperature goes down and the temperature at high altitude rises. Zhang et al. [ 112 ] found that the aerosol optical depth (AOD) at 500 nm in North China reaches 0.60–1.00 during the pollution period, where the fine-mode particles contribute more than 90% to the aerosol extinction characteristics and the single-scattering albedo of the aerosol is lower than 0.88. Hu and Liu [ 113 ] pointed out that there is a negative correlation between PM 2.5 concentration and total surface radiation. Especially at noon, the correlation coefficient can reach − 0.62. In addition, in September and December, it was found that an increase of 1 µ g · m − 3 in PM 2.5 concentration would cause a decrease in total radiation of 1.8 W/m 2 and 0.5 W/m 2 , a drop of ground surface temperature by 0.11 ◦ C and 0.02 ◦ C, and a drop of air temperature by 0.03 ◦ C and 0.01 ◦ C. Wu et al. [ 114 ] studied the impact of PM 2.5 on the urban heat island (UHI) and found that higher PM 2.5 concentrations leads to lower UHI intensity, especially during the daytime and the UHI can be reduced by up to 1 K The changes in the concentration of particulate matter can affect the formation processes of cloud and rainfall, and indirectly affect climate change. In the formation of rain, it is necessary to have a nucleus of condensation in order to form raindrops from water vapor. Other than salt in the seawater, the sources of nucleus of condensation come from PM 2.5 . If there are too many particles, they may “eat away water”, so that the raindrops in the sky are not growing, then, drizzle and clear weather days will become less than before. On the other hand, the existence of PM 2.5 might help to increase the number of condensation nuclei, so that possibility of rainfall will increase, and extreme rainstorm can even be produced. Therefore, in areas with heavy precipitation, PM 2.5 may encourage precipitation and bring more rainfall; while in areas with little precipitation, it may help to reduce rainfall. Simulation from Gui et al. [ 115 ] showed that the increase of aerosol particulates in different regions of China resulted in decreased air temperatures at the height under 2 m, decreased humidity ratio and precipitation in most parts of eastern China Yao et al. [ 116 ] studied air pollution in the Jing-Jin-Ji region and its impact on evapotranspiration (ET) They suggested that PM 2.5 concentration has a significant negative effect on ET in most cities and that amount of water for agricultural irrigation could be reduced at high PM 2.5 concentrations. In addition, PM 2.5 can also aggravate or mitigate the acidification of rainwater in the pollution area, depending on the major components of the ions contained in PM 2.5 . Li and Zhang [ 117 ] sampled and analyzed data on precipitation and PM 2.5 in Xi’an in 2011, and found that PM 2.5 in Xi’an is acidic, which is in consistent with the pH value of the precipitation 3.3. Impact on Human Health As early as in the 1980 s, a large number of epidemiological studies abroad have shown that PM 2.5 has obvious side effects on human health [ 118 – 121 ]. The studies in China on the relationship between PM 2.5 and human health also fully proved that PM 2.5 can cause increases in the incidence of pulmonary heart disease [ 122 ], respiratory disease [ 123 ], cardiovascular disease [ 124 ], cancer [ 125 , 126 ] and other diseases, and even the death risk [ 127 – 129 ]. Long-term exposure to ambient PM 2.5 might be an important risk factor of hypertension and is responsible for significant hypertension burden in adults in China [ 130 , 131 ], and it leads to reduced lung function [ 132 , 133 ]. PM 2.5 is a risk factor for asthma [ 134 , 135 ], and it was related to the onset of children cough variant asthma by reducing immune regulation and ventilatory function [ 136 ]. PM 2.5 exposures might affect reproductive health Significantly decreased fertility rates by 2.0% per 10 µ g · m − 3 increment of PM 2.5 were observed in [ 137 ]. Wu et al. [ 138 ] found that ambient PM exposure during sperm development adversely affects semen quality, in particular sperm concentration and count. However, Zhou et al. [ 139 ] argued that air PM 10 and PM 10–2.5 (2.5 ≤ cut sizes ≤ 10 µ m) exposures, not PM 2.5 , are risk factors of semen quality In addition, the indoor PM 2.5 exposure levels were positively associated with skin aging manifestation, including score of pigment spots on forehead and wrinkle on upper lip [ 140 ]. PM 2.5 may lead to induced DNA damage and cell cycle arrest in lung tumorigenesis [ 141 ]. Repeated exposure to PM 2.5

[Find the meaning and references behind the names: Ways, Acs, Haze, Jia, Xie, Delivery, Care, Hospital, Million, Pay, Mood, Age, Habit, Mask, Ouyang, Living, Room, Rate, Chen, Half, Cope, Mental, Cheng]

Int. J. Environ. Res. Public Health 2018 , 15 , 438 13 of 29 induces vascular inflammation [ 142 ]. Measles incidence was found to be associated with exposure to ambient PM 2.5 [ 143 ]. Significant associations between PM 2.5 and acute coronary syndrome (ACS) have also been found in most studies [ 144 ]. PM 2.5 may induce oxidative stress and inflammatory responses in human nasal epithelial cells, thereby leading to nasal inflammatory diseases [ 145 ]. Ambient PM 2.5 concentrations were significantly associated with influenza-like-illness risk [ 146 ]. PM 2.5 is associated with mortality. There are papers and reports on PM 2.5 sources and associated mortality in China as part of the Global Burden of Disease (GBD). It was estimated that the global premature mortality by PM 2.5 was at 3.15 million/year in 2010 with China being the leading country with about 1.33 million [ 147 ]. Lin et al. [ 148 ] found significant associations between PM 2.5 daily exceedance concentration hours (DECH) and cardiovascular mortality (3.0–5.02% increase in mortality rate per 500 µ g · m − 3 increase in PM 2.5 ). Health burden study by Song et al. [ 127 ] suggested that PM 2.5 in 2015 contributed as much as 40.3% to total stroke deaths, 33.1% to acute lower respiratory infection (ALRI, <5 years) deaths, 26.8% to ischemic heart disease (IHD) deaths, 23.9% to lung cancer (LC) deaths, 18.7% to chronic obstructive pulmonary disease (COPD) deaths, 30.2% to total deaths combining IHD, stroke, COPD, and LC, 15.5% to all cause deaths. Electronic hospitalization summary reports derived from 26 major cities in China between 1 January 2014 and 31 December 2015 showed that PM 2.5 had a negative impact on incidence of delirium, which is an independent risk factor for morbidity and mortality among older surgical adults [ 149 ]. The non-accidental mortality rate increases with exposure to extreme weather condition, especially hot dry synoptic weather types (SWT) and warm humid [ 150 ]. The effects of ambient air pollution and temperature triggered out-of-hospital coronary deaths (OHCDs) in China [ 151 ]. It was found that there is a spatial correlation between the mortality of respiratory diseases in Chinese provinces, corresponding to the spatial effect of PM 2.5 pollutions [ 152 ] Some scholars have conducted studies on specific groups of people. For example, Li et al. [ 153 ] studied the relationship between pregnant women’s exposure to PM 2.5 and the birth weight of newborns. Cheng et al. [ 154 ] pointed out that exposure of pregnant women in the third trimester, especially half a month before delivery, to high concentrations of PM 2.5 , will lead to an increased risk of preterm birth. It might also be associated with low birth weight (LBW) and small for gestational age (SGA) [ 155 ]. Chen et al. [ 156 ] showed that the allergenicity in children is potentially related to the indoor PM 2.5 component and its content by comparing the toxicity of cells of allergic and non-allergic children exposed to indoor PM 2.5 . Tu [ 157 ] pointed out there is an impact of PM 2.5 in Nanchang on the increase in outpatient pediatric respiratory disease outbreaks, with a maximum cumulative lag effect of 5 days. A 10 µ g · m − 3 increase of PM 2.5 concentration in the atmosphere resulted in 0.43% increase of respiratory disease outpatient visits. Ouyang et al. [ 158 ] found that the PM 2.5 concentrations are positively correlated with pneumonia hospitalization number of children, and their effect on boys is more obvious than that in the girls. PM 2.5 was independently associated with the risk of intensive care unit admission due to pneumonia (ICUp), and the maximum effect occurred at 3 to 4 days after exposure [ 159 ]. There were positive correlation between high concentrations of PM 2.5 and increasing daily emergency room visits [ 160 ]. In addition, PM 2.5 might also affect people’s mental health. When exposed in haze weather for a long time, people could easily become depressed; in severe cases depression might also be induced. Study from Jia et al. [ 161 ] indicated that PM 2.5 exposure might negatively affect mood regulation and increase the risk of mental disorder 3.4. Impact on Human Behavior PM 2.5 is considered to be the “culprit” that causes hazy weather. It is harmful to people’s health and at the same time has affected all aspects of people’s living conditions. First of all, to cope with the frequent smog weather, people pay more and more attention to the prevention of PM 2.5 inhalation. Wearing an anti-haze mask has become a popular habit in China. Gu and Xie [ 162 ] pointed out that in haze weather residents would go out with anti-haze masks and would selectively adjust their outdoor activities or change their ways of transportation depending on the outside air conditions. Residents will change their window opening behavior, such as window opening time and size to prevent PM 2.5 penetration into the

[Find the meaning and references behind the names: Life, Field, Hour, Ability, Wall, Comfort, Ach]

Int. J. Environ. Res. Public Health 2018 , 15 , 438 14 of 29 room [ 163 , 164 ]. Of course, turning on anti-haze air conditioners is also a preferred option due to their ability to reduce the PM 2.5 concentration while maintaining high level of indoor thermal comfort [ 165 ] In addition, the hazy weather is disruptive to the effects of many scenic landscapes, and therefore many tourists will change their travel decisions during hazy weather [ 166 ] 4. Indoor and Outdoor PM 2.5 Relationship 4.1. Current Indoor PM 2.5 Pollution Status According to a survey on the life style of the residents, people in China spent 85% of their time indoors, of which 50% of their time was spent inside the buildings. In particular, the elderly spent 90% of their time indoors, of which 76% of their time was in the residential buildings [ 167 ]. Therefore, an indoor environment with an acceptable indoor PM 2.5 mass concentration level is an essential prerequisite for healthy living of the residents. At present, there are not many researches on indoor PM 2.5 pollution in China. However, it can be concluded that existing PM 2.5 pollution in China is very serious (Table 3 ). Compared with the daily average limit of PM 2.5 level of 35 µ g · m − 3 based on China’s latest ambient air quality standard, the PM 2.5 concentration level in almost all of the buildings in the cities studied in Table 3 exceed the limit. In some heavily polluted public spaces, the PM 2.5 concentration can exceed the limit by more than five times 4.2. Ease of Indoor Environment Contaminated by Outdoor PM 2.5 Some researchers have suggested that outdoor PM 2.5 can enter the room through three ways, including natural ventilation, mechanical ventilation and infiltration [ 168 ]. For natural ventilation, the outdoor PM 2.5 was driven by wind pressure and thermal pressure into the interior of the building, and often there is no filter to remove the particle matters. For buildings with central air conditioning system, the fresh air was introduced by mechanical ventilation and go through air filters, however, the filters cannot removed all the particle matters and hence the PM 2.5 can enter the interior environment. The infiltration is related to air tightness. Due to the existence of cracks in the building envelope, the outdoor PM 2.5 will penetrate through the crack even when the doors and windows are fully closed. It is very important to evaluate how easily the indoor environment can be contaminated by outdoor PM 2.5 . Currently, the indoor and outdoor particle concentration ratio (I/O ratio) and penetration coefficient are considered as two important parameters to be used for evaluation 4.2.1. I/O Ratio Most of the studies on I/O ratios were carried out based on field data measurement under natural ventilation or infiltration (see Table 4 ). From Table 4 , it is found that the I/O ratios obtained by different researchers vary greatly. It could be due to the differences in the outdoor pollution level, outdoor weather conditions (outdoor wind speed, wind direction, temperature, humidity ratio, etc.), indoor sources of pollution, the conditions of the building envelope itself (the airtightness of the outer windows, the degree of sealing performance of the outer window with the wall, cracks over the wall, etc.), and the air changes per hour (ACH) [ 76 , 82 , 169 – 175 ]. Lin et al. [ 173 ] explored the difference of PM 2.5 pollution in Wuhan and Guangzhou. Strong seasonal variation patterns were found, and PM 2.5 pollution in Wuhan was more serious than that in Guangzhou. Through sampling data analysis, Zhou [ 174 ] found significant negative correlation among PM 2.5 mass concentration, temperature, and wind speed. Significant positive correlation between PM 2.5 mass concentration and relative humidity, and relatively weak relationship between PM 2.5 mass concentration and atmospheric pressure were found. Wang et al. [ 175 ] found the indoor PM 2.5 concentrations were affected by the outdoor PM 2.5 concentration and the degree of air tightness of the outer windows. Under the same outdoor PM 2.5 concentration, the outer windows with higher air-tightness were less prone to be affected by the outdoor PM 2.5 .

[Find the meaning and references behind the names: Place, Mall, Market, Court]

Int. J. Environ. Res. Public Health 2018 , 15 , 438 15 of 29 Table 3. Current status of indoor PM 2.5 pollution Building Type Sampling Location Sampling Condition Average Indoor PM 2.5 Concentration ( µ g · m − 3 ) References Times Exceeding Limit Set by Standard Public place Chongqing Business hour 211 (68–468) [ 176 ] 6.03 Public place Ma’anshan Business hour 133.73 (74.96–259.28) [ 177 ] 3.82 Residential building Lanzhou Daily routine Kitchen: 124.75 (48.14–279.25); Bedroom: 118.91 (38.34–367.62) [ 178 ] 3.56; 3.40 Residential building Nanjing No cooking, no smoking 80 (47–113) [ 179 ] 2.29 Hospital Shenzhen Business hour 36.71 (4.98–318.01) [ 180 ] 1.05 Government agency Tianjin Business hour 71.0 (1–380) [ 181 ] 2.03 Shopping mall Beijing Business hour 47 (9–253) [ 182 ] 1.34 Market Beijing Business hour 56.21–61.36 [ 183 ] 1.61–2.25 Food court Nanchang Business hour 164 (38.03–492.73) [ 184 ] 4.69

[Find the meaning and references behind the names: Mild, Peak]

Int. J. Environ. Res. Public Health 2018 , 15 , 438 16 of 29 Table 4. I/O (indoor/outdoor) ratio under different ventilation modes Ventilation Mode Reference Sampling Time Period Building Type Impact Factors of I/O Ratio Results Natural ventilation [ 169 ] 1 December 2013–28 February 2014 Residential building Outdoor PM 2.5 mass concentration level When the outdoor PM 2.5 concentration is in the ranges of 0–33 µ g · m − 3 , 34–65 µ g · m − 3 , 66–129 µ g · m − 3 , and ≥ 130 µ g · m − 3 , the I/O ratios are 1.75, 1.05, 0.76 and 0.63, respectively [ 170 ] April–December 2015 (one week per month, except in July and August) School Outdoor PM 2.5 mass concentration level, ACH, wind speed and outdoor air temperature The time average I/O is 0.69. It varies in the range of 0.1–5.46. The I/O ratio decreases with the increases of outdoor PM 2.5 mass concentration level [ 171 ] 09:00–18:00, 13–15 March 2014 Laboratory complex building ACH 48.7–57.3% of the PM 2.5 pollutants come from indoor sources and the I/O ratio varies 0.90–1.23 Infiltration [ 172 ] September 2013– August 2014 Office Outdoor dry bulb temperature, relative humidity ratio and wind speed The average ACH is 0.10 under mild weather, 0.22 when the wind speed is 1.6–3.4 m/s, 0.39 when the wind speed is 5.5–8.0 m/s. The corresponding I/O ratios are 0.43, 0.56 and 0.62, respectively [ 82 ] Winter, 2014 Residential building Indoor pollution sources When the indoor PM 2.5 concentration reached its peak value, the I/O ratio was 0.67–0.89 [ 76 ] June 2013–August 2013; December 2013– February 2014 Office Seasonal changes, wind speed and relative humidity ratio The indoor and outdoor PM 2.5 concentrations in winter were higher than those in summer and the corresponding I/O ratios were also higher in winter than in summer ACH: air changes per hour.

[Find the meaning and references behind the names: Deal, Cao, Enough, Rid, Return, Fight, Far, Foreign, Thatcher, Run, Case, Worth, Flow, Short, Focus]

Int. J. Environ. Res. Public Health 2018 , 15 , 438 17 of 29 4.2.2. Penetration Coefficient Through surveys on people’s window opening behavior, it is found that 60% of the people select to close the window under haze weather condition to prevent the outdoor PM 2.5 from entering the indoor environment [ 185 ]. In the case of closing the doors and windows, study on the penetration of PM 2.5 through the envelope cracks becomes particularly important. Some researches advised that the outdoor PM 2.5 entering the building envelope through the cracks is the process of “penetration” [ 168 , 185 – 187 ], where “penetration coefficient” is the decisive factor to evaluate the rate of PM 2.5 entering the indoor Many foreign scholars obtained the penetration coefficients of fine particles through experimental measurements [ 187 – 189 ], e.g., Thatcher et al. [ 188 ] found that the penetration coefficient is larger for smaller fine particles. Some scholars in China have also conducted researches on determining the penetration coefficient. Their studies mainly focus on some influencing factors that affect the penetration coefficient of fine particles, such as the height of the crack [ 190 ], the roughness of the inner surface of the crack [ 191 , 192 ], the indoor/outdoor pressure difference [ 192 ], crack geometry [ 193 ], ACH [ 194 ] and so on. Due to the limitations of available devices and testing conditions, only a small number of studies in China currently focus on studying the penetration coefficient of PM 2.5 alone Based on previous studies, Li [ 185 ] discussed on the outer window penetration coefficient of PM 2.5 is affected by multiple factors, including particle size, indoor/outdoor pressure difference, air exchange rate, and geometry and surface roughness of cracks in the building envelopes 5. Indoor PM 2.5 Control 5.1. Air Filter and Air Conditioner Combination Since the outdoor PM 2.5 pollution cannot be gotten rid of in the short run, it is important to control the indoor PM 2.5 pollution level to reduce its impact on occupants’ health. Some researches focused on selection of certain combination of air filters. For example, Cao et al. [ 195 ] developed indoor PM 2.5 pollution control model under mechanical ventilation, and advised on how to select certain combination of air filters with different particulate removal efficiencies for a central AC system Tu et al. [ 196 ] conducted test on filter efficiency based on particle sizing and counting method and PM 2.5 weight filtration method for multiple air filters of different materials and different particle removal efficiencies under the same experimental conditions. The relationship between these two filtration efficiencies provides a preliminary basis for the selection of PM 2.5 air filter for indoor air conditioning and ventilation system. Wang [ 197 ] studied the filtration performance of different grades of PM 2.5 filters and proposed suitable filter combination schemes based on the PM 2.5 pollution status in different regions. Comprehensive evaluations on the performance of different filter combination schemes were conducted, which could be used as references for the design of primary air conditioning system. Based on the principle of mass conservation, Lv [ 198 ] developed an indoor PM 2.5 concentration model of the primary return air-conditioning system and studied the impact of the changes of the filtration efficiency and the fresh air flow rate on the indoor PM 2.5 concentration, when the filters are installed in the primary air section, return air section and supply air section, respectively. The results from these researches can only be used for the primary air supply of the central air-conditioning system. It is worth mentioning that split air conditioner systems are installed in most of the residential buildings in China for indoor environment control. The split system is a ductless system. It has an outdoor unit and an indoor unit, where the inside (evaporative) heat exchanger is separated from the outside (condensing unit) heat exchanger. No fresh air systems are equipped. In general, the measures taken by the residents in China to deal with outdoor PM 2.5 pollution are to fully close the doors and windows Hence, no fresh air can be treated by air filters and PM 2.5 can still penetrate through the cracks. It is far from enough to fight with PM 2.5 pollution by simply closing the doors and windows, so how to maintain a healthy indoor environment for buildings with split air conditioning system remains a problem to be solved in China.

[Find the meaning and references behind the names: Khalid, Jet, Force, Turn, Gree, Blow, Back, Cost, Red, Mean, Ion, Kind, Color, Blue, Pass]

Int. J. Environ. Res. Public Health 2018 , 15 , 438 18 of 29 5.2. Development of New Material for Air Filters Some researchers dedicated to the development of new filter materials For example, Zhao et al. [ 199 ] reported that high efficiency and low resistance air purification materials made by electrospun polyvinylidene fluoride fiber (PVDF) doping with negative ion powders (NIPs) can have purification efficiency of up to 99.9%. Zhang et al. [ 200 ] utilized high-thermal-stability polyimide nanofibers to develop a highly effective polyimide nanofiber air filter. The efficiency of the filter to remove PM 2.5 from automobile exhaust at high temperatures can reach 99.5%. Li et al. [ 201 ] developed a reusable polyethersulfone hollow fiber membrane with high permeability using single-dry-jet wet-spinning technology. These filter materials have a high PM 2.5 capturing capacity, and if they can be widely used in air conditioning system, the burden to remove the indoor PM 2.5 will be greatly alleviated. However, due to the high initial investment cost, it is unrealistic to widely adopt this kind of filters in China Other researchers developed filters that can be attached to the window to allow air to flow through to reduce the filtering cost. For example, Liu et al. [ 202 ] introduced a polyacrylonitrile transparent filter that captures the PM through controlling the surface chemistry and microstructure of the air filters. It allows natural, passive ventilation to pass through the window and can achieve removal efficiency of up to 98.69% at transmittance of ~77% in haze weather. Zhao et al. [ 203 ] reported slip-effect functional nanofibrous membranes with purification efficiency of 99.09% and transmittance of 77% with low air resistance of 29.5 Pa. Khalid et al. [ 204 ] reported a blow-spinning technique for large scale coating of nanofiber transparent air filter on window screen which achieved standard PM 2.5 removal efficiency of >99% with 80% optical transparency. However, the study from Shi et al. [ 205 ] found that the mean value of harmonic average air exchange rate when the windows are open is far below the national standard. Therefore, more measures are needed to be taken to further reduce the filter resistance to enhance natural ventilation 5.3. Anti-Haze Room Air Conditioners Available in the Market Frequent episodes of hazy weather remind the residents of the importance and urgency to improve air quality, and it has become a driving force for the traditional air-conditioners to be upgraded with PM 2.5 purification functiond. Table 5 lists some of the popular AC products from different air conditioner companies with PM 2.5 purification function, which come from Midea, Haier, Panasonic, Gree and KELON. For example, the air conditioner (AC) products of Midea utilize a washable PM 2.5 purification module with an electronic generator to create an electric field in the dust collection device to captured charged particles, which effectively removes PM 2.5 . The air conditioning products of Haier use visualization function to capture PM 2.5 . Each AC unit is equipped with a 5-color indicator When indoor PM 2.5 level exceeds the high limit, the indicator turns red and urge the occupants to turn on the PM 2.5 removal function, and it becomes blue when the indoor PM 2.5 level is back to normal Panasonic air conditioners release negatively charged “nanoe-G” to be absorbed by PM 2.5 in the air, through which PM 2.5 is negatively charged and collected by electric field with high efficiency. The air conditioning products of Kelon use “three processes” (stripping technology, packaging technology and melting technology) for PM 2.5 purification. The air-conditioners listed are residential models, effective in room size of 10–50 m 2 (2650–7200 W).

[Find the meaning and references behind the names: Brand, Grade]

Int. J. Environ. Res. Public Health 2018 , 15 , 438 19 of 29 Table 5. Air conditioner with efficient PM 2.5 purification function Brand Type Capacity (W) Energy Grade Main PM 2.5 Removal Technology PM 2.5 Removal Efficiency Panasonic KFR-36 GW/BpSJ 1 S 3600 3 PM 2.5 air filter 84% Haier KFR-50 LW/16 UCP 22 AU 1 5300 2 PET antibacterial and anti-mildew air filter 99% Gree KFR-26 GW/(26571)FNBh-1 2650 1 Group filters with strong PM 2.5 capturing ability, primary air filter and high efficiency air filter ≥ 97% KELON KFR-72 LW/EFVEA 2(2 N 01) 7200 2 Inhibitory fins that inhibit the growth of 99.9% bacteria ≥ 99% Midea KFR-35 GW/BP 3 DN 1 Y-QA 100 3500 1 Washable PM 2.5 purifying module and dust collecting device 90%

[Find the meaning and references behind the names: Eng, Carry, Conception, Liang, Sci, Final, Gov, Read, Trend, Rep, Grant, Author]

Int. J. Environ. Res. Public Health 2018 , 15 , 438 20 of 29 6. Conclusions The problem of PM 2.5 pollution in China is severe. It has seriously threatened the health of the residents. As compared with the developed countries, studies on PM 2.5 in China are still lagging behind. However, with the establishment of air quality standards for PM 2.5 in recent years and development of PM 2.5 monitoring stations nationwide in China, research on PM 2.5 in China has gradually been enhanced. The studies on PM 2.5 have increased exponentially, and more and more disciplines have got involved in the study on PM 2.5 , which are mainly related to PM 2.5 source analysis, the impact of PM 2.5 on human health, relationship between indoor and outdoor PM 2.5 pollution levels and indoor PM 2.5 control. It is worth mentioning that there are several measurement methods and small differences in the measurement results of PM 2.5 concentration might be found. Most of the researches on I/O ratio and percentage of outdoor source contributions were based on the weighting method, which is a direct and reliable method. Generally speaking, the studies discussed throughout the paper will not be greatly affected with different measurement methods in various researches There are still many shortcomings with current researches. For example, some studies only collected data in the typical month of the season or even a few days in a typical month. Although the data could be representative, they may also be incidental. Nowadays, depression, autism and other psychological diseases frequently occur, however, little research of PM 2.5 impact on mental health can be found in China. Most of the researches on the indoor and outdoor pollution correlations are conducted only for a specific building, i.e., under specific physical and meteorological condition [ 75 – 79 ], for example, in a residential apartment [ 75 ], or in an office building [ 76 ]. Although many useful results have been obtained from these studies, they depend largely on the experimental conditions at the time when data were collected and cannot be applied to more general situations. In addition, although the environmental monitoring network of PM 2.5 in the outdoor atmosphere is already established in China, the establishment of monitoring network on indoor air PM 2.5 , which is more closely related to human health, is still lagging behind. It may be due to the facts that the indoor PM 2.5 pollution concentration limit is not clear in China. At the same time, the cost of creating household monitoring network is high, and it is difficult to carry out long time, standardized, PM 2.5 monitoring and data collection indoor Acknowledgments: Natural Science Foundation of Hubei Province under grant [2017 CFB 602] and Hunan Provincial Department of housing and urban rural development under grant [KY 2016063] Author Contributions: Yaolin Lin contributed to the conception and organization of the study. Yaolin Lin, Jiale Zou, Wei Yang and Chun-Qing Li wrote the manuscript. All the authors have read and approved the final manuscript Conflicts of Interest: The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results References 1 Zhang, S.; Wang, Y.; Li, Y.; Zhang, P. Spatial distribution of haze pollution and its influencing factors China Popul. Resour. Environ 2017 , 27 , 15–22. [ CrossRef ] 2 Ministry of Environmental Protection of the People’s Republic of China. Bulletin on Environmental Conditions in China in 2015. Available online: http://www.mep.gov.cn/hjzl/zghjzkgb/lnzghjzkgb/201606/ P 020160602333160471955.pdf (accessed on 19 December 2017) 3 Ministry of Environmental Protection of the People’s Republic of China. Bulletin on Environmental Conditions in China in 2016. Available online: http://www.mep.gov.cn/hjzl/zghjzkgb/lnzghjzkgb/201706/ P 020170605833655914077.pdf (accessed on 19 December 2017) 4 Liang, Z.; Ma, M.; Du, G. Comparison of characteristics and trend analysis of atmospheric pollution in Beijing–Tainji–Shijiazhuang during 2003–2012 Environ. Eng 2014 , 12 , 76–81 5 Zhang, Y.L.; Cao, F. Fine particulate matter (PM 2.5 ) in China at a city level Sci. Rep 2015 , 5 , 14884. [ CrossRef ] [ PubMed ] 6 Yang, F.; Ma, Y.; He, K. Research on General Situation of PM 2.5 World Environ 2000 , 4 , 32–35.

[Find the meaning and references behind the names: Yue, Forest, Borne, Board, Europa, Nea, Dong, Zeng, Gao, Cass, Phe, Kiang, Salmon, Htm, Teng, Trace, Xiao, Apps, Jian, Chin, Epa, Zhu, Pang, Chan, Zhuang, Nic, Aap, Schauer, Shang, Tao, Female, Tang, Nat, Iris]

Int. J. Environ. Res. Public Health 2018 , 15 , 438 21 of 29 7 WHO. WHO’s Urban Ambient Air Pollution Database 2016. Available online: http://www.who.int/phe/ health_topics/outdoorair/databases/who-aap-database-may 2016.xlsx (accessed on 10 December 2017) 8 US EPA. National Ambient Air Quality Standards (NAAQS). Available online: https://www.epa.gov/sites/ production/files/2015-02/documents/criteria.pdf (accessed on 24 December 2017) 9 Australian Government, Department of the Environment and Energy. Ambient Air Quality Standards Available online: http://www.environment.gov.au/protection/air-quality/air-quality-standards (accessed on 24 December 2017) 10 WHO. WHO Air Quality Guidelines for Particulate Matter, Ozone, Nitrogen Dioxide and Sulfur Dioxide Available online: http://apps.who.int/iris/bitstream/10665/69477/1/WHO_SDE_PHE_OEH_06.02_eng. pdf (accessed on 24 December 2017) 11 European Commission. Air Quality Standards. Available online: http://ec.europa.eu/environment/air/ quality/standards.htm (accessed on 24 December 2017) 12 National Environment Agency. Air Quality and Targets. Available online: http://www.nea.gov.sg/anti-pollutionradiation-protection/air-pollution-control/air-quality-and-targets (accessed on 24 December 2017) 13 Ministry of the Environment, Government of Japan. Environmental Quality Standards in Japan—Air Quality Available online: http://www.env.go.jp/en/air/aq/aq.html (accessed on 24 December 2017) 14 Ministry of Environment, Forest, and Climate Change, Government of India. National Ambient Air Quality Standards Central Pollution Control Board Notification. Available online: http://www.moef.nic.in/sites/ default/files/notification/Recved%20 national.pdf (accessed on 24 December 2017) 15 Department of Science, Technology and Standards, Ministry of Environmental Protection of the People’s Republic of China. Ambient Air Quality Standards. Available online: http://kjs.mep.gov.cn/hjbhbz/bzwb/ dqhjbh/dqhjzlbz/201203/W 020120410330232398521.pdf (accessed on 24 December 2017) 16 Wu, G.; Hu, W.; Teng, E.; Wei, F. PM 2.5 and PM 10 pollution level in the four cities in China China Environ. Sci 1999 , 2 , 133–137 17 Wang, W.; Tang, D.; Liu, H.; Yue, X.; Pang, Z.; Ding, Y. Research on Current Pollution Status and Pollution Characteristics of PM 2.5 in China Res. Environ. Sci 2000 , 13 , 1–6. [ CrossRef ] 18 He, K.; Yang, F.; Ma, Y.; Zhang, Q.; Yao, X.; Chan, C.K.; Cadle, S.; Chan, T.; Mulawa, P. The characteristics of PM 2.5 in Beijing, China Atmos. Environ 2001 , 35 , 4959–4970. [ CrossRef ] 19 Yang, F.; He, K.; Ma, Y. Chemical characteristics of PM 2.5 species in Beijing ambient air J. Tsinghua Univ. Nat Sci. Ed 2002 , 42 , 1605–1608 20 Huang, L.; Wang, G.; Wang, H.; Gao, S.; Wang, L. Pollution level of the airborne particulate matter (PM 10 , PM 2.5 ) in Nanjing City China Environ. Sci 2002 , 22 , 334–337. [ CrossRef ] 21 Wang, H.; Wang, G.; Gao, S.; Wang, L. Characteristics of atmospheric particulate pollution in spring in Nanjing City China Environ. Sci 2003 , 23 , 55–59. [ CrossRef ] 22 Yang, F.; He, K.; Ma, Y.; Chen, X.; Cadle, S.H.; Chan, T.; Mulawa, P.A. Characteristics and Sources of Trace Elements in Ambient PM 2.5 in Beijing Environ. Sci 2003 , 24 , 33–37. [ CrossRef ] 23 Jian, Z.; Song, W.; Zhou, X.; Zhang, Y. Study on mouse pulmonary acute injury induced by air-borne PM 2.5 J. Hyg. Res 2004 , 33 , 264–266 24 Dong, C.; Song, W.; Shi, Y. Study on the oxidative injury of the vascular endothelial cell affected by PM 2.5 J. Hyg. Res 2005 , 34 , 169–171. [ CrossRef ] 25 Huang, N.H.; Wang, Q.; Xu, D.Q. Immunological effect of PM 2.5 on cytokine production in female Wistar rats Biomed. Environ. Sci 2008 , 21 , 63–68. [ CrossRef ] 26 Zhu, L.; Xiao, Y.; Chen, J.; Zhou, H.; Zhang, X. Pollution Status and Cytotoxicity of PM 2.5 in Hangzhou City J. Environ. Health 2009 , 2 , 147–148 27 Xiao, C.; Li, S.; Shang, D.; Zhu, X.; Chen, D.; Wang, R. Pathologic changes in trachea of rats exposed to PM 2.5 artificial air pollution Chin. J. Public Health 2011 , 12 , 1579–1581 28 Dong, C. Study on Toxic Effects of PM 2.5 on Cardiovascular System. Ph.D. Thesis, Fudan University, Shanghai, China, 2005 29 Dan, M.; Zhuang, G.; Li, X.; Tao, H.; Zhuang, Y. The characteristics of carbonaceous species and their sources in PM 2.5 in Beijing Atmos. Environ 2004 , 38 , 3343–3452. [ CrossRef ] 30 Zheng, M.; Salmon, L.G.; Schauer, J.J.; Zeng, L.; Kiang, C.S.; Zhang, Y.; Cass, G.R. Seasonal trends in PM 2.5 source contributions in Beijing, China Atmos. Environ 2005 , 39 , 3967–3976. [ CrossRef ]

[Find the meaning and references behind the names: Lee, South, Real, Wen, Chai, Chu, Cha, Lain, Master, Dou, Luo, Gong, Guan, Yin, Mater, Feng, Bai, Cui, Dev, Yun, Lian, Peng, Hazard, Fog]

Int. J. Environ. Res. Public Health 2018 , 15 , 438 22 of 29 31 Wang, Y.; Zhuang, G.; Zhang, X.; Huang, K.; Xu, C.; Tang, A.; Chen, J.; An, Z. The ion chemistry, seasonal cycle, and sources of PM 2.5 and TSP aerosol in Shanghai Atmos. Environ 2006 , 40 , 2935–2952. [ CrossRef ] 32 Song, Y.; Tang, X.; Xie, S.; Zhang, Y.; Wei, Y.; Zhang, M.; Zeng, L.; Lu, S. Source apportionment of PM 2.5 in Beijing in 2004 J. Hazard. Mater 2007 , 146 , 124–130. [ CrossRef ] [ PubMed ] 33 Dong, X.; Sun, J. Source analysis of PM 2.5 in Wuhan area J. Liaoning Tech. Univ. Sci. Ed 2009 , 28 , 125–127 34 Ye, W. Study on source apportionment of PM 10 and PM 2.5 in ambient air of Ningbo Res. Environ. Sci 2011 , 33 , 66–69. [ CrossRef ] 35 Yang, F.; Duan, F.; He, K. PM 2.5 speciation sampling and analysis methods Environ. Monit. China 2004 , 20 , 14–19. [ CrossRef ] 36 Yang, F.M.; Ye, B.M.; He, K.B.; Ma, Y.L.; Cadle, S.H.; Chan, T.; Mulawa, P.A. Characterization of Atmospheric Mineral Components of PM 2.5 in Beijing and Shanghai, China Sci. Total Environ 2005 , 343 , 221–230 [ CrossRef ] [ PubMed ] 37 Sun, Y.L.; Zhuang, G.S.; Tang, A.H.; Wang, Y.; An, Z.S. Chemical characteristics of PM 2.5 and PM 10 in haze-fog episodes in Beijing Environ. Sci. Technol 2006 , 40 , 3148–3155. [ CrossRef ] [ PubMed ] 38 Lain, S.C.; Zou, S.C.; Cao, J.J.; Lee, S.C.; Ho, K.F. Characterizing ionic species in PM 2.5 and PM 10 in four Pearl River Delta cities, South China J. Environ. Sci 2007 , 19 , 939–947. [ CrossRef ] 39 Tan, J. Chemical characteristics of PM 2.5 during a typical haze episode in Guangzhou J. Environ. Sci 2009 , 21 , 774–781. [ CrossRef ] 40 Chen, K.; Yin, Y.; Wei, Y.; Yang, W. Characteristics of carbonaceous aerosols in PM 2.5 in Nanjing China Environ. Sci 2010 , 8 , 1015–1020 41 Yang, T. Chemical Compositions and Source Apportionment of PM 2.5 in Changsha. Master’s Thesis, Central South University, Changsha, China, 2010 42 Gu, J.; Bai, Z.; Li, W.; Wu, L.; Liu, A.; Dong, H.; Xie, Y. Chemical composition of PM 2.5 during winter in Tianjin, China Particuology 2011 , 3 , 215–221. [ CrossRef ] 43 Huang, X.; Yun, H.; Guan, Z.; Li, X.; He, L.; Zhang, Y.; Hu, M. Source apportionment and secondary organic aerosol estimation of PM 2.5 in an urban atmosphere in China Sci. Sin. Terrae 2014 , 44 , 723–734. [ CrossRef ] 44 Hu, M.; Tang, Q.; Peng, J.; Wang, E.; Wang, S.; Cha, F. Study on Characterization and Source Apportionment of Atmospheric Particulate Matter in China Environ. Sustain. Dev 2011 , 5 , 15–19. [ CrossRef ] 45 Zheng, J.; Zhang, Y.; Yang, C. Review of PM 2.5 Source Apportionment Methods in China J. Peking Univ. Sci. Ed 2014 , 50 , 1141–1154. [ CrossRef ] 46 Huang, X.; Yun, H.; Gong, Z.; Li, X.; He, L.; Zhang, Y.; Hu, M. Air pollution characteristic and variation trend of Central Triangle urban agglomeration from 2005 to 2014 Sci. Sin. Terrae 2014 , 44 , 723–734 47 Wang, J.; Bi, X.; Feng, Y.; Zhang, Y.; Wu, J.; Lv, A. Pollution characteristics and source apportionment of PM 2.5 during heavy pollution process in Urumchi City Res. Environ. Sci 2014 , 27 , 113–119 48 Zhao, X.; Wang, X.; Chu, Y.; Yang, W.; Ren, L.; Bai, Z. Characterization of chemical composition mass balance and source appointment of ambient PM 2.5 in Xinzhou city Chin. J. Environ. Eng 2017 , 11 , 4660–4668 49 Xiao, Z.; Bi, X.; Feng, D.; Wang, Y.; Zhou, J.; Fu, X.; Wen, Y. Source Apportionment of Ambient PM 10 and PM 2.5 in Urban Area of Ningbo City Environ. Sci. Res 2012 , 25 , 549–555 50 Tang, M. Tianjin Ambient Air PM 2.5 Source Apportionment. Master’s Thesis, Nankai University, Tianjin, China, 2014 51 Ren, L.; Zhou, Z.; Zhao, X.; Yang, W.; Yin, B.; Bai, Z.; Ji, Y. Source Apportionment of PM 10 and PM 2.5 in Urban Areas of Chongqing Environ. Sci. Res 2014 , 27 , 1387–1394 52 Yang, Y.; Li, J.; Lian, Y.; Chen, T.; Liu, B.; Sun, F.; Cheng, G.; Su, J.; Zhang, D. Source apportionment of PM 2.5 in Beijing by the chemical mass balance Acta Sci. Circumst 2015 , 35 , 2693–2700. [ CrossRef ] 53 Dou, Y.; Zhao, X.; Xu, X.; Gao, H.; Li, T.; Ding, M.; Liu, Y.; Han, B.; Bai, Z. Source Apportionment of PM 2.5 in Xining by the Chemical Mass Balance Environ. Monit. China 2016 , 32 , 7–14. [ CrossRef ] 54 Chen, F.; Yu, H.; Hu, J.; Chai, F. Analysis of Source of PM 2.5 in Xingtai Using Chemical Mass model J. Ecol. Rural Environ 2017 , 33 , 1075–1083. [ CrossRef ] 55 Cheng, H.; Wang, Z.; Feng, J.; Chen, H.; Zhang, F.; Liu, J. Carbonaceous species composition and source apportionment of PM 2.5 in urban atmosphere of Wuhan Ecol. Environ 2012 , 21 , 1574–1579 56 Zhang, Z.; Tao, J.; Xie, S.; Zhou, L.; Song, D.; Zhang, P.; Cao, J.; Luo, L. Seasonal variations and source apportionment of PM 2.5 at urban area of Chengdu J. Environ. Sci. Circumst 2013 , 33 , 2947–2952 57 Wang, X.; Zhao, Q.; Cui, H. PM 2.5 source apportionment at suburb of Shanghai in winter based on real time monitoring J. Nanjing Univ. Sci. Ed 2015 , 51 , 517–523. [ CrossRef ]

[Find the meaning and references behind the names: Handan, Ivey, Transport, Yuan, Aes, Zhi, Zong, Qiao, Sui, Dai, Hua, Guang, Zhai, Russell, Meng, Guo, Tian, Yan, Shen, Ying]

Int. J. Environ. Res. Public Health 2018 , 15 , 438 23 of 29 58 Zong, Z.; Wang, X.; Tian, C.; Chen, Y.; Lin, Q.; Ji, L.; Zhi, G.; Li, J.; Zhang, G. Source apportionment of PM 2.5 at a regional background site in North China using PMF linked with radiocarbon analysis: Insight into the contribution of biomass burning Atmos. Chem. Phys 2016 , 16 , 11249–11265. [ CrossRef ] 59 Tan, J.; Zhang, L.; Zhou, X.; Duan, J.; Li, Y.; Hu, J.; He, K. Chemical characteristics and source apportionment of PM 2.5 in Lanzhou, China Sci. Total Environ 2017 , 601–602 , 1743–1752. [ CrossRef ] [ PubMed ] 60 Chen, Y.; Xie, S.D.; Luo, B.; Zhai, C.Z. Particulate pollution in urban Chongqing of southwest China: Historical trends of variation, chemical characteristics and source apportionment Sci. Total Environ 2017 , 584–585 , 523–534. [ CrossRef ] [ PubMed ] 61 Yao, L.; Yang, L.; Yuan, Q.; Yan, C.; Dong, C.; Meng, C.; Sui, X.; Yang, F.; Lu, Y.; Wang, W. Sources apportionment of PM 2.5 in a background site in the North China Plain Sci. Total Environ 2016 , 541 , 590–598 [ CrossRef ] [ PubMed ] 62 Wang, F.; Lin, T.; Feng, J.; Fu, H.; Guo, Z. Source apportionment of polycyclic aromatic hydrocarbons in PM 2.5 using positive matrix factorization modeling in Shanghai, China Environ. Sci. Process. Impacts 2015 , 17 , 197–205. [ CrossRef ] [ PubMed ] 63 Wang, J. Chemical Composition Characteristics and Source Apportionment of PM 2.5 in Zhengzhou Ph.D. Thesis, Zhengzhou University, Zhengzhou, China, 2015 64 Zhou, Y.; Zhang, H.; Wang, Q.; Xu, L.; Wang, C. Pollution characteristics and source apportionment of PM 2.5 from Qinshan District in Wuhan during the winter Environ. Sci. Technol 2015 , 38 , 159–164 65 Chen, X.; Du, P.; Guang, Q.; Feng, X.; Xu, D.; Lin, S. Application of ICP-MS and ICP-AES for Studying on Source Apportionment of PM 2.5 during Haze Weather in Urban Beijing Spectrosc. Spectr. Anal 2015 , 35 , 1724–1729. [ CrossRef ] 66 Ma, X.; Wang, L.; Ma, S.; Wei, Z.; Zhang, C.; Zheng, A. Spatial and temporal distribution and source analysis of components in PM 2.5 , Handan Environ. Chem 2017 , 36 , 1932–1940 67 Meng, C.; Wang, L.; Su, J.; Yang, J.; Wei, Z.; Zhang, F.; Ma, S. Chemical compositions and source apportionment of PM 2.5 in Handan City, Hebei Province Environ. Sci. Technol 2016 , 39 , 57–64 68 Cui, H.; Chen, W.; Dai, W.; Liu, H.; Wang, X.; He, K. Source apportionment of PM 2.5 in Guangzhou combining observation data analysis and chemical transport model simulation Atmos. Environ 2015 , 116 , 262–271. [ CrossRef ] 69 Wang, L.; Bi, X.; Liu, B.; Wu, J.; Zhang, Y.; Feng, Y.; Zhang, Q. Source Directional Apportionment of PM 2.5 in Heze City Environ. Sci. Res 2017 , 30 , 1849–1858 70 Shi, G.; Liu, J.; Wang, H.; Tian, Y.; Wen, J.; Shi, X.; Feng, Y.; Ivey, C.E.; Russell, A.G. Source apportionment for fine particulate matter in a Chinese city using an improved gas-constrained method and comparison with multiple receptor models Environ. Pollut 2018 , 233 , 1058–1067. [ CrossRef ] [ PubMed ] 71 Qiao, X.; Ying, Q.; Li, X.; Zhang, H.; Hu, J.; Tang, Y.; Chen, X. Source apportionment of PM 2.5 for 25 Chinese provincial capitals and municipalities using a source-oriented Community Multiscale Air Quality model Sci. Total Environ 2018 , 612 , 462–471. [ CrossRef ] [ PubMed ] 72 Jin, X.; Zhang, G.; Xiao, C.; Huang, D.; Yuan, G.; Yao, Y.; Wang, X.; Hua, L.; Wang, P.; Ni, B. Source Apportionment of PM 2.5 in Xinzhen Beijing Using PIXE and XRF At. Energy Sci. Technol 2014 , 48 , 1325–1330 73 Zhang, Y.; Zhang, W.; Wang, J. Establishment and application of pollutant Inventory-Chemical Mass Balance (I-CMB) model for source apportionment of PM 2.5 Trans. Atmos. Sci 2015 , 38 , 279–284. [ CrossRef ] 74 Shi, J.; Yuan, D.; Zhao, Z. Residential indoor PM 2.5 sources, concentration and influencing factors in China J. Environ. Health 2015 , 32 , 825–829 75 Han, Y.; Qi, M.; Chen, Y.; Shen, H.; Liu, J.; Huang, Y.; Chen, H.; Liu, W.; Wang, W.; Liu, J.; et al. Influences of ambient air PM 2.5 concentration and meteorological condition on the indoor PM 2.5 concentrations in a residential apartment in Beijing using a new approach Environ. Pollut 2015 , 205 , 307–314. [ CrossRef ] [ PubMed ] 76 Zhao, L.; Chen, C.; Wang, P.; Wang, Y.; Chen, Z.; Wang, Q.; Lu, B.; Cao, G.; Meng, C.; Wang, L.; et al. Characteristics of Change of PM 2.5 Mass Concentration Indoors and Outdoors in an Office Building in Beijing in Summer and Winter Build. Sci 2015 , 31 , 32–39. [ CrossRef ] 77 Du, T.; Yao, W.; Zhang, S.; Zhou, Z. Influencing Factors of Outdoor PM 2.5 Mass Concentration and Indoor Control Measures Gas Heat 2016 , 36 , 21–25. [ CrossRef ] 78 Wang, F.; Meng, D.; Li, X.; Tan, J. Indoor-outdoor relationships of PM 2.5 in four residential dwellings in winter in the Yangtze River Delta, China Environ. Pollut 2016 , 215 , 280–289. [ CrossRef ] [ PubMed ]

[Find the meaning and references behind the names: Adm, Xiong, Fang, Bao, Inn, Hang, Zuo, Mong, Fan, Rui, Mao, Pan, Laser, Fung, Lei, Lau, Deng, Qiu]

Int. J. Environ. Res. Public Health 2018 , 15 , 438 24 of 29 79 Ji, W.; Zhao, B. Contribution of outdoor-originating particles, indoor-emitted particles and indoor secondary organic aerosol (SOA) to residential indoor PM 2.5 concentration: A model-based estimation Build. Environ 2015 , 90 , 196–205. [ CrossRef ] 80 Zhang, S.; Duan, Y. Ditermination the PM 2.5 concentration in the room of Liting mosquito-repellent incense and cigarette Inn. Mong. Environ. Sci 2013 , 25 , 184–185. [ CrossRef ] 81 Li, T.; Cao, S.; Fan, D.; Zhang, Y.; Wang, B.; Zhao, X.; Leaderer, B.P.; Shen, G.; Zhang, Y. Duan, X. Household concentrations and personal exposure of PM 2.5 among urban residents using different cooking fuels Sci. Total Environ 2016 , 548–549 , 6–12. [ CrossRef ] [ PubMed ] 82 Zhou, Z.; Liu, Y.; Yuan, J.; Zuo, J.; Chen, G.; Xu, L.; Rameezdeen, R. Indoor PM 2.5 concentrations in residential buildings during a severely polluted winter: A case study in Tian, China Renew. Sustain. Energy Rev 2016 , 64 , 372–381. [ CrossRef ] 83 Gui, F.; Ye, Q.; Zhou, Y.; Huang, H. Influences of sweeping on the Concentration of Particulate Matter in the indoor air J. Anhui Univ. Technol. Nat. Sci 2013 , 30 , 250–254 84 Sun, H.; Lin, Z.; Mao, H. PM 2.5 Emission from Laser Printers in an Environmental Chamber Build. Energy Environ 2016 , 35 , 44–48 85 Zhang, N.; Han, B.; He, F.; Xu, J.; Zhao, R.; Zhang, Y.; Bai, Z. Chemical characteristic of PM 2.5 emission and inhalational carcinogenic risk of domestic Chinese cooking Environ. Pollut 2017 , 227 , 24–30. [ CrossRef ] [ PubMed ] 86 Xue, W.; Fu, F.; Wang, J.; Tang, G.; Lei, Y.; Yang, J.; Wang, Y. Numerical study on the characteristics of regional transport of PM 2.5 in China China Environ. Sci 2014 , 34 , 1361–1368 87 Wu, D.; Fung, J.C.H.; Yao, T.; Lau, A.K.H. A study of control policy in the Pearl River Delta region by using the particulate matter source apportionment method Atmos. Environ 2013 , 76 , 147–161. [ CrossRef ] 88 Lü, W.; Li, J.F.; Wang, X.S.; Zhang, Y.H. Numerical modeling on the impact of long-range transport of air pollutants on the regional air quality in the Pearl River Delta Acta Sci. Circumst 2015 , 35 , 30–41. [ CrossRef ] 89 Li, T.Y.; Fan, S.J.; Deng, X.J.; Zhang, X.B.; Deng, T.; Li, T. Study on the regional transported effect of PM 2.5 in Guangzhou area J. Univ. Chin. Acad. Sci 2014 , 31 , 403–409. [ CrossRef ] 90 Wang, Y.; Xue, W.; Lei, Y.; Wang, J.; Wu, W. Regional Transport Matrix Study of PM 2.5 in Jingjinji Region, 2015 Environ. Sci 2017 , 38 , 4897–4904. [ CrossRef ] 91 Wang, Z.; Li, Y.; Chen, T.; Zhang, D.; Sun, F.; Pan, L. Spatial-temporal characteristics of PM 2.5 in Beijing in 2013 Acta Geogr. Sin 2015 , 70 , 110–120 92 Zhang, L.; Wu, J.; Bao, Y.; Li, J.; Yu, C. Study on the temporal and spatial distribution characteristics of PM 2.5 in Xi’an city Air Pollut. Control 2016 , 34 , 87–90 93 Zhao, C.X.; Wang, Y.Q.; Wang, Y.J.; Zhang, H.L.; Zhao, B. Temporal and Spatial Distribution of PM 2.5 and PM 10 Pollution Status and the Correlation of Particulate Matters and Meteorological Factors During Winter and Spring in Beijing Environ. Sci 2014 , 2 , 418–427 94 Su, Z.; Wang, J. Pollution Characteristics and Determinants of Atmospheric Particulate Matter and Its Determinants in Guiyang Acta Sci. Nat. Univ. Sunyatseni 2015 , 54 , 77–84. [ CrossRef ] 95 Wang, H.; Wang, S.; Guo, Z.; Han, Z.; Gong, X.; Li, Y. Study on the Variation Characteristics of SO 2 , PM 10 , and PM 2.5 Concentrations in the Urban and Surburban Areas during Winter in Taiyuan Environ. Sustain. Dev 2014 , 39 , 190–193 96 Song, Y.; Tang, X.; Zhang, Y.; Hu, M.; Fang, C.; Zeng, L.; Wang, W. Effects on Fine Particles by the Continued High Temperature Weather in Beijing Environ. Sci 2002 , 23 , 33–36. [ CrossRef ] 97 Zheng, X.; Zhao, W.; Yan, X.; Zhao, W.; Xiong, Q. Spatial and temporal variation of PM 2.5 in Beijing city after rain Ecol. Environ. Sci 2014 , 23 , 797–805. [ CrossRef ] 98 Jiang, D.; Li, C. Relationship of the Diffusion of PM 2.5 and Meteorological Conditions in Nanjing Urban Area Adm. Tech. Environ. Monit 2016 , 28 , 36–40. [ CrossRef ] 99 Wei, Y.X.; Yin, Y.; Yang, W.F.; Rui, D.M.; Hang, W.Q. Analysis of the Pollution Characteristics & Influence Factors of PM 2.5 in Nanjing Area Environ. Sci. Manag 2009 , 34 , 29–34. [ CrossRef ] 100. Chen, T.; He, J.; Lu, X.W.; She, J.F.; Guan, Z.Q. Spatial and Temporal Variations of PM 2.5 and Its Relation to Meteorological Factors in the Urban Area of Nanjing, China Int. J. Environ. Res. Public Health 2016 , 13 , 921 [ CrossRef ] [ PubMed ] 101. Zhang, C.; Wang, M.; Hu, Z.; Yuan, Y.; Liu, H.; Qiu, F. Temporal and spatial distribution of PM 2.5 concentration and the correlation of PM 2.5 and meteorological factors in Kunming City J. Yunnan Univ 2016 , 38 , 90–98. [ CrossRef ]

[Find the meaning and references behind the names: Connect, Shu, Chiang, Abbey, Dis, Kong, Qian, Riemer, Hsu, Wilfrid, Yeh, Shao, Xing, Land, Petersen, Pope, Xia, Lines, Iii, Kabir, Cai, Kim, Brauer, Heath, Boman, Hong, Chang, Med, Station, Geng, Arid, Tong, Che, Liao]

Int. J. Environ. Res. Public Health 2018 , 15 , 438 25 of 29 102. Wang, H.C.; Wu, Z.B.; Zhou, J.B.; Bao, J. Relationship between PM 2.5 concentration and meteorological elements at Shangdianzi station of Beijing J. Meteorol. Environ 2015 , 31 , 99–104 103. Zhang, S.P.; Han, L.J.; Zhou, W.Q.; Zheng, X.X. Relationships between fine particulate matter (PM 2.5 ) and meteorological factors in winter at typical Chinese cities Acta Ecol. Sin 2016 , 36 , 7897–7907. [ CrossRef ] 104. Wu, D.; Liu, Q.; Lian, Y.; Bi, X.; Li, F.; Tan, H.; Liao, B.; Chen, H. Hazy weather formation and visibility deterioration resulted from fine particulate (PM 2.5 ) pollutions in Guangdong and Hong Kong J. Environ. Sci. Circumst 2012 , 32 , 2660–2669 105. Ma, Z.; Zhao, X.; Meng, W.; Meng, Y.; He, D.; Liu, Y. Comparison of Influence of Fog and Haze on Visibility in Beijing Environ. Sci. Res 2012 , 25 , 1208–1214 106. Zhang, H.; Shi, C.; Wu, B.; Yang, Y. Quantified Relationships among the Visibility, Relative Humidity and PM 2.5 Mass Concentration in Hefei City Ecol. Environ 2017 , 26 , 1001–1008. [ CrossRef ] 107. Yao, Q.; Han, X.; Cai, Z.; Zhang, M. Study on characteristic of aerosol extinction at Tianjin City in Spring China Environ. Sci 2012 , 32 , 795–802. [ CrossRef ] 108. Hao, J.; Zhang, G.; Yang, Y. The characteristics of atmospheric visibility and influencing factors J. Arid Land Resour. Environ 2017 , 31 . [ CrossRef ] 109. Chen, J.; Qiu, S.S.; Shang, J.; Wilfrid, O.M.F.; Liu, X.G.; Tian, H.Z.; Boman, J. Impact of Relative Humidity and Water Soluble Constituents of PM 2.5 on Visibility Impairment in Beijing, China Aerosol Air Qual. Res 2014 , 14 , 260–268. [ CrossRef ] 110. Wang, J.L.; Zhang, Y.H.; Shao, M.; Liu, X.L.; Zeng, L.M.; Cheng, C.L.; Xu, X.F. Quantitative relationship between visibility and mass concentration of PM 2.5 in Beijing J. Environ. Sci 2006 , 3 , 475–481. [ CrossRef ] 111. Shi, H.D.; Gao, Q.X.; Zhang, S.H.; Chen, D.S.; Du, W.P.; Fu, J.F.; Bai, H.M. Research review of impacts and feedback of air pollution on climate change Res. Environ. Sci 2012 , 25 , 974–980 112. Zhang, X.; Xia, X.; Che, H.; Tang, J.; Tang, Y.; Meng, W.; Dong, P. Observation Study on Aerosol Optical Properties and Radiative Forcing Using the Ground-based and Satellite Remote Sensing at Background Station during the Regional Pollution Episodes Environ. Sci 2014 , 35 , 2439–2448 113. Hu, S.L.; Liu, H.N. Effects of PM 2.5 on the urban radiation and air temperature in Hefei J. Meteorol. Sci 2017 , 1 , 78–85. [ CrossRef ] 114. Wu, H.; Wang, T.; Riemer, N.; Chen, P.; Li, M.; Li, S. Urban heat island impacted by fine particles in Nanjing, China Sci. Rep 2017 , 7 , 11422. [ CrossRef ] [ PubMed ] 115. Gui, Z.; Zheng, Y.; Qian, Z.; He, H.; Zeng, X. Numerical study of indirect aerosols effect on regional climate over eastern China J. Nanjing Univ. Nat. Sci 2014 , 50 , 781–791. [ CrossRef ] 116. Yao, L. Causative impact of air pollution on evapotranspiration in the North China Plain Environ. Res 2017 , 158 , 436–442. [ CrossRef ] [ PubMed ] 117. Li, F.; Zhang, C. Analysis on the Relationship between PM 2.5 and the Precipitation in Xi’an Environ. Monit. China 2013 , 29 , 22–28. [ CrossRef ] 118. Abbey, D.E.; Ostro, B.E.; Petersen, F.; Burchette, R.J. Chronic respiratory symptoms associated with estimated long-term ambient concentrations of fine particulates less than 2.5 microns in aerodynamic diameter (PM 2.5 ) and other air pollutants J. Expo. Anal. Environ. Epidemiol 1995 , 5 , 137–196. [ PubMed ] 119. Churg, A.; Brauer, M. Human lung parenchyma retains PM 2.5 Am. J. Respir. Crit. Care Med 1997 , 155 , 2109–2111. [ CrossRef ] [ PubMed ] 120. Pope, C.A., III; Dockey, D.W. Heath effects of fine particulate air pollution: Lines that connect J. Air Waste Manag. Assoc 2006 , 56 , 709–742. [ CrossRef ] [ PubMed ] 121. Kim, K.H.; Kabir, E.; Kabir, S. A review on the human health impact of airborne particulate matter Environ. Int 2015 , 74 , 136–143. [ CrossRef ] [ PubMed ] 122. Xu, J.; Zhang, Z.; Geng, H.; Qiu, Y.; Tong, G.; Han, J.; Zhao, Y.; Liu, J. Relationship between PM 2.5 exposure and pulmonary function in different working environments J. Environ. Health 2013 , 30 , 1–4 123. Xing, Y.; Xu, Y.; Shi, M.; Lian, Y. The impact of PM 2.5 on the human respiratory system J. Thorac. Dis 2016 , 8 , 69–74. [ CrossRef ] 124. Duan, Y.; Huang, Z.; Shu, Z.; Yuan, H. Ambient PM 2.5 during pregnancy and risk on preterm birth Chin. J. Cardiol 2014 , 44 , 179–182. [ CrossRef ] 125. Yeh, H.L.; Hsu, S.W.; Chang, Y.C.; Chan, T.C.; Tsou, H.C.; Chang, Y.C.; Chiang, P.H. Spatial Analysis of Ambient PM 2.5 Exposure and Bladder Cancer Mortality in Taiwan Int. J. Environ. Res. Public Health 2017 , 14 , 508 [ CrossRef ] [ PubMed ]

[Find the meaning and references behind the names: Mol, Williams, Xiang, Kamp, Yeoh, Pilot, Rong, Male, Leo, Dose, Tam, Woo, Vaughn, Morgan, Cummings, Wong, Blood, Hwang, Jaakkola, Howard, Beas, Hou, Akt, Chuang, Young]

Int. J. Environ. Res. Public Health 2018 , 15 , 438 26 of 29 126. Shu, Y.; Zhu, L.; Yuan, F.; Chen, E.; Chen, L. Analysis of the relationship between PM 2.5 and lung cancer based on protein-protein interactions Comb. Chem. High Throughput Screen 2016 , 19 , 100–108. [ CrossRef ] [ PubMed ] 127. Song, C.; He, J.; Wu, L.; Jin, T.; Chen, X.; Li, R.; Ren, P.; Zhang, L.; Mao, H. Heath burden attributable to ambient PM 2.5 in China Environ. Pollut 2017 , 223 , 575–586. [ CrossRef ] [ PubMed ] 128. Wu, J.; Zhu, J.; Li, W.; Xu, D.; Liu, J. Estimation of the PM 2.5 health effects in China during 2000–2011 Environ. Sci. Pollut. Res 2017 , 224 , 10695–10707. [ CrossRef ] [ PubMed ] 129. Shi, T.; Dong, H.; Yang, T.; Jian, Q.; Hu, G.; Feng, W.; Lv, J.; Lin, H. Association between PM 2.5 air pollution and daily resident mortality in Guangzhou urban area in winter J. Environ. Health 2015 , 32 . [ CrossRef ] 130. Zhang, Z.; Guo, C.; Lau, A.K.H.; Chan, T.C.; Chuang, Y.C.; Lin, C.; Jiang, W.K.; Yeoh, E.K.; Tam, T.; Woo, K.S.; et al. Long-Term Exposure to Fine Particulate Matter, Blood Pressure, and Incident Hypertension in Taiwanese Adults Environ. Health Perspect 2018 , 126 , 017008. [ CrossRef ] [ PubMed ] 131. Lin, H.; Guo, Y.; Zheng, Y.; Di, Q.; Liu, T.; Xiao, J.; Li, X.; Zeng, W.; Cummings-Vaughn, L.A.; Howard, S.W.; et al. Long-Term Effects of Ambient PM 2.5 on Hypertension and Blood Pressure and Attributable Risk Among Older Chinese Adults Hypertension 2017 , 69 , 806–812. [ CrossRef ] [ PubMed ] 132. Xu, T.; Hou, J.; Cheng, J.; Zhang, R.; Yin, W.; Huang, C.; Zhu, X.; Chen, W.; Yuan, J. Estimated individual inhaled dose of fine particles and indicators of lung function: A pilot study among Chinese young adults Environ Pollut 2018 , 235 , 505–513. [ CrossRef ] [ PubMed ] 133. Hwang, B.F.; Chen, Y.H.; Lin, Y.T.; Wu, X.T.; Leo Lee, Y. Relationship between exposure to fine particulates and ozone and reduced lung function in children Environ. Res 2015 , 137 , 382–390. [ CrossRef ] [ PubMed ] 134. Chen, Y.; Wong, G.W.; Li, J. Environmental Exposure and Genetic Predisposition as Risk Factors for Asthma in China Allergy Asthma Immunol. Res 2016 , 8 , 92–100. [ CrossRef ] [ PubMed ] 135. Liu, T.; Wu, B.; Wang, Y.; He, H.; Lin, Z.; Tan, J.; Yang, L.; Kamp, D.W.; Zhou, X.; Tang, J.; et al. Particulate matter 2.5 induces autophagy via inhibition of the phosphatidylinositol 3-kinase/Akt/mammalian target of rapamycin kinase signaling pathway in human bronchial epithelial cells Mol. Med. Rep 2015 , 12 , 1914–1922. [ CrossRef ] [ PubMed ] 136. Zhang, Y.X.; Liu, Y.; Xue, Y.; Yang, L.Y.; Song, G.D.; Zhao, L. Correlational study on atmospheric concentrations of fine particulate matter and children cough variant asthma Eur. Rev. Med. Pharmacol. Sci 2016 , 20 , 2650–2654. [ PubMed ] 137. Xue, T.; Zhang, Q. Associating ambient exposure to fine particles and human fertility rates in China Environ. Pollut 2018 , 235 , 497–504. [ CrossRef ] [ PubMed ] 138. Wu, L.; Jin, L.; Shi, T.; Zhang, B.; Zhou, Y.; Zhou, T.; Bao, W.; Xiang, H.; Zuo, Y.; Li, G.; et al Association between ambient particulate matter exposure and semen quality in Wuhan, China Environ. Int 2017 , 98 , 219–228. [ CrossRef ] [ PubMed ] 139. Zhou, N.; Jiang, C.; Chen, Q.; Yang, H.; Wang, X.; Zou, P.; Sun, L.; Liu, J.; Li, L.; Huang, L.; et al. Exposures to Atmospheric PM 10 and PM 10–2.5 Affect Male Semen Quality: Results of MARHCS Study Environ. Sci. Technol 2018 , 52 , 1571–1581. [ CrossRef ] [ PubMed ] 140. Ding, A.; Yang, Y.; Zhao, Z.; Hüls, A.; Vierkötter, A.; Yuan, Z.; Cai, J.; Zhang, J.; Gao, W.; Li, J.; et al Indoor PM 2.5 exposure affects skin aging manifestation in a Chinese population Sci. Rep 2017 , 7 , 15329 [ CrossRef ] [ PubMed ] 141. Wu, J.; Shi, Y.; Asweto, C.O.; Feng, L.; Yang, X.; Zhang, Y.; Hu, H.; Duan, J.; Sun, Z. Fine particle matters induce DNA damage and G 2/M cell cycle arrest in human bronchial epithelial BEAS-2 B cells Environ. Sci. Pollut. Res. Int 2017 , 24 , 25071–25081. [ CrossRef ] [ PubMed ] 142. Chen, S.; Wu, X.; Hu, J.; Dai, G.; Rong, A.; Guo, G. PM 2.5 exposure decreases viability, migration and angiogenesis in human umbilical vein endothelial cells and human microvascular endothelial cells Mol. Med. Rep 2017 , 16 , 2425–2430. [ CrossRef ] [ PubMed ] 143. Chen, G.; Zhang, W.; Li, S.; Williams, G.; Liu, C.; Morgan, G.G.; Jaakkola, J.J.K.; Guo, Y. Is short-term exposure to ambient fine particles associated with measles incidence in China? A multi-city study Environ. Res 2017 , 156 , 306–311. [ CrossRef ] [ PubMed ] 144. Meng, X.; Zhang, Y.; Yang, K.Q.; Yang, Y.K.; Zhou, X.L. Potential Harmful Effects of PM 2.5 on Occurrence and Progression of Acute Coronary Syndrome: Epidemiology, Mechanisms, and Prevention Measures Int. J. Environ. Res. Public Health 2016 , 13 , 748. [ CrossRef ] [ PubMed ]

[Find the meaning and references behind the names: De Wilde, Gan, Bottai, Niu, Jiao, Panel, Six, Novel, Wilde, Nan, Homes, Smart, Cold, Simul, Meta, Ban, Zhong]

Int. J. Environ. Res. Public Health 2018 , 15 , 438 27 of 29 145. Hong, Z.; Guo, Z.; Zhang, R.; Xu, J.; Dong, W.; Zhuang, G.; Deng, C. Airborne Fine Particulate Matter Induces Oxidative Stress and Inflammation in Human Nasal Epithelial Cells Tohoku J. Exp. Med 2016 , 239 , 117–125 [ CrossRef ] [ PubMed ] 146. Feng, C.; Li, J.; Sun, W.; Zhang, Y.; Wang, Q. Impact of ambient fine particulate matter (PM 2.5 ) exposure on the risk of influenza-like-illness: A time-series analysis in Beijing, China Environ. Health 2016 , 15 . [ CrossRef ] [ PubMed ] 147. Giannadaki, D.; Lelieveld, J.; Pozzer, A. Implementing the U.S. air quality standard for PM 2.5 worldwide can prevent millions of premature deaths per year Environ. Health 2016 , 15 . [ CrossRef ] [ PubMed ] 148. Lin, H.; Wang, X.; Qian, Z.M.; Guo, S.; Yao, Z.; Vaughn, M.G.; Dong, G.; Liu, T.; Xiao, J.; Li, X.; et al Daily exceedance concentration hours: A novel indicator to measure acute cardiovascular effects of PM 2.5 in six Chinese subtropical cities Environ. Int 2017 , 111 , 117–123. [ CrossRef ] [ PubMed ] 149. Che, L.; Li, Y.; Gan, C. Effect of short-term exposure to ambient air particulate matter on incidence of delirium in a surgical population Sci. Rep 2017 , 7 , 15461. [ CrossRef ] [ PubMed ] 150. Fang, X.; Fang, B.; Wang, C.; Xia, T.; Bottai, M.; Fang, F.; Cao, Y. Relationship between fine particulate matter, weather condition and daily non-accidental mortality in Shanghai, China: A Bayesian approach PLoS ONE 2017 , 12 , e 0187933. [ CrossRef ] [ PubMed ] 151. Dai, J.; Chen, R.; Meng, X.; Yang, C.; Zhao, Z.; Kan, H. Ambient air pollution, temperature and out-of-hospital coronary deaths in Shanghai, China Environ. Pollut 2015 , 203 , 116–121. [ CrossRef ] [ PubMed ] 152. Cao, Q.; Liang, Y.; Niu, X. China’s Air Quality and Respiratory Disease Mortality Based on the Spatial Panel Model Int. J. Environ. Res. Public Health 2017 , 14 , 1081. [ CrossRef ] [ PubMed ] 153. Li, C.; Lu, F.; Liu, Y.; Chen, C. Association between maternal exposure to fine particulate matter and low birth weight: A meta analysis J. Environ. Health 2017 , 34 , 38 154. Chen, Y.; Feng, Y.; Duan, X.; Zhao, N.; Wang, J.; Li, C.; Guo, P.; Xie, B.; Zhang, F. Ambient PM 2.5 during pregnancy and risk on preterm birth Chin. J. Epidemiol 2016 , 37 , 572–577. [ CrossRef ] 155. Zhu, X.; Liu, Y.; Chen, Y.; Yao, C.; Che, Z.; Cao, J. Maternal exposure to fine particulate matter (PM 2.5 ) and pregnancy outcomes: A meta-analysis Environ. Sci. Pollut. Res. Int 2015 , 22 , 3383–3396. [ CrossRef ] [ PubMed ] 156. Chen, J.; Wu, Y.; Jiao, M.; Yang, X.; Ma, P.; Yuan, B. Comparison of the cytotoxicity of the indoor PM 2.5 from allergic or from non-allergic children’s homes: An in vitro study J. Environ. Sci. Circumst 2017 . [ CrossRef ] 157. Tu, H. The Characteristics of PM 2.5 and the Relationship between PM 2.5 and Children Health in Urban Areas in Nanchang. Master’s Thesis, Nanchang University, Nanchang, China, 2016 158. Ouyang, F.; Liu, S.; Mao, J.; Zheng, Q.; Ma, T.; Hu, M. Relationship between air pollution and the number of pneumonia hospitalization in a children’s hospital in Changsha Zhong Nan Da Xue Xue Bao Yi Xue Ban 2017 , 42 , 1417–1424. [ CrossRef ] [ PubMed ] 159. Zhang, Z.; Hong, Y.; Liu, N. Association of ambient Particulate matter 2.5 with intensive care unit admission due to pneumonia: A distributed lag non-linear model Sci. Rep 2017 , 7 , 8679. [ CrossRef ] [ PubMed ] 160. Wang, X.; Li, G.; Jin, X.; Mu, J.; Pan, J.; Liang, F.; Tian, L.; Chen, S.; Guo, Q.; Dong, W.; et al. Study of relationship between atmospheric fine particulate matter concentration and one grade a tertiary hospital emergency room visits during 2012 and 2013 in Beijing Zhonghua Yu Fang Yi Xue Za Zhi 2016 , 50 , 73–78 [ CrossRef ] [ PubMed ] 161. Jia, Z.; Wei, Y.; Li, X.; Yang, L.; Liu, H.; Guo, C.; Zhang, L.; Li, N.; Guo, S.; Qian, Y.; et al. Exposure to Ambient Air Particles Increases the Risk of Mental Disorder: Findings from a Natural Experiment in Beijing Int. J. Environ. Res. Public Health 2018 , 15 , 160. [ CrossRef ] [ PubMed ] 162. Gu, J.; Xie, H. Investigation on Risk Cognition and Responding Behavior towards Haze of Nanjing Residents Environ. Sci. Manag 2015 , 40 , 178–187 163. Shi, S.; Zhao, B. Occupants’ interaction with windows in 8 residential apartments in Beijing and Nanjing, China Build. Simul 2015 , 9 . [ CrossRef ] 164. Pang, S.; Xu, C.; Wei, S.; MHassan, T.; Xie, L.; Xiong, Y.; de Wilde, P. Research on the Occupant Window Opening Behavior in an Office Building in Beijing Build. Sci 2015 , 31 , 212–217. [ CrossRef ] 165. Su, L. Healthy Air Supply-Smart Sensing Technology Leading New Direction of Air Conditioning into the Cold 2018 Househ. Appl 2017 , 9 , 82–83 166. Nan, Y.; Sun, J. Effects of Haze on the Tourist Decision-Making J. Guangzhou Univ. Soc. Sci. Ed 2015 , 10 , 35–41 167. Liu, J. Study on Healthy Risk Assessment & Simulation on Residential Indoor Environmental Based on Stochastic Theory. Ph.D. Thesis, Hunan University, Changsha, China, 2007.

[Find the meaning and references behind the names: Leung, Shell, Rough, John, Chao, Brown, Smooth, Wedge, Lai, Furniture, Tung, Vent]

Int. J. Environ. Res. Public Health 2018 , 15 , 438 28 of 29 168. Chen, C.; Zhao, B. Review of relationship between indoor and outdoor particles: I/O ratio, infiltration factor and penetration factor Atmos. Environ 2011 , 45 , 275–288. [ CrossRef ] 169. Du, Y.; Zhang, Y.; Li, T. Residential indoor exposures of PM 2.5 and relationship between indoor and outdoor PM 2.5 in winter in Beijing J. Environ. Health 2016 , 4 , 268–283. [ CrossRef ] 170. Zhang, M.; Qian, H.; Zheng, X.; Zhang, H. Analysis of indoor and outdoor PM 2.5 mass concentration and influencing factors of a kindergarten in Nanjing Heat. Vent. Air Cond 2012 , 46 , 20–25 171. Xie, W.; Fan, Y.; Huang, Y.; Li, L. The Characteristic of Indoor and Outdoor PM 10 , PM 2.5 , PM 1.0 Pollution of Natural Ventilation Contam. Control Air-Cond. Technol 2015 , 3 , 51–55. [ CrossRef ] 172. Chen, C.; Wang, Y.; Chen, Z.; Wang, Y.; Wu, Y.; Zhao, L. Dynamic Characteristics in Air Infiltration Rate with Respect to Atmospheric PM 2.5 Pollution J. Beijing Univ. Technol 2017 , 2 , 285–293. [ CrossRef ] 173. Lin, C.; Liao, Q.; Huang, F. Investigation on the Influencing Factors of PM 2.5 Concentration. In Proceedings of the Annual Meeting of Chinese Society for Environmental Sciences, Haikou, China, 13–14 October 2016 174. Zhou, L. Research on Affecting Factors of PM 2.5 Contam. Control Air-Cond. Technol 2017 , 1 . [ CrossRef ] 175. Wang, Q.; Li, G.; Meng, C.; Zhao, L.; Wang, J.; Wangm, X. Penetration of outdoor fine particulate matter (PM 2.5 ) through building envelope and passive control methods Heat. Vent. Air Cond 2015 , 12 , 8–13 176. Xu, C.; Wang, Q.; Li, N.; Chang, J.; Lin, X.; Xu, D. Investigation of indoor fine particles in public places and influencing factors J. Environ. Health 2014 , 11 , 993–996 177. Liu, Y.; Cao, J.; Chen, F.; Chen, J.; Zhang, Z.; Lu, Y. Characteristics of air pollution in various indoor public places Chin. J. Public Health 2016 , 36 . [ CrossRef ] 178. Fan, D.; Cao, S.; Zhang, Y.; Huang, N.; Dong, T.; Zhao, X.; Li, T.; Duan, X.; Zhang, W. Preliminary study on indoor PM 2.5 pollution levels of residents in Lanzhou during heating period J. Environ. Health 2014 , 3 , 232–234 179. Wang, Y.; Cui, L.; Zhou, L.; Wu, J.; Zhang, X.; Ding, Z.; Chen, X.; Ji, Q.; Zhou, Y.; Wang, T.; et al. Investigation of indoor PM 2.5 and PM 1.0 in some houses in Nanjing J. Environ. Health 2013 , 10 , 900 180. Li, J.; Yu, S.; Liu, G.; Huang, G.; Wang, W.; Zhang, Z. Indoor Air Concentrations of PM 2.5 in Hospitals in Shenzhen J. Environ. Health 2012 , 4 , 330–331 181. Li, W.; Wang, G.; Zheng, W.; Yang, Y.; Pan, Y. Hygienic survey about indoor PM 2.5 and air nicotine in main public places in Tianjin J. Environ. Health 2014 , 31 , 787–789 182. Shen, F.; Jia, Y.; Zhang, Q.; Zhao, R.; Huang, L.; Li, R.; Zhang, G.; Guo, Q. Indoor PM 2.5 pollution level and influencing factors in public places in Beijing in winter J. Environ. Health 2014 , 31 , 262–263 183. Zhang, J.; Yu, S.; Song, M. Study on the Pollution Level of Formaldehyde and PM 2.5 in Indoor Environment of Furniture and Clothing Markets Build. Sci 2016 , 32 , 21–26 184. Chen, L.; Fan, Y.; Yang, S.; Wu, J.; Li, Z.; Chen, H. Investigation of indoor air PM 2.5 concentration in public places in Nanchang J. Hyg. Res 2014 , 1 , 146–148 185. Li, G. Study on the PM 2.5 Penetration through Exterior Windows under Different Conditions and Control Methods. Ph.D. Thesis, China Academy of Building Research, Beijing, China, 2016 186. Li, G.; Wang, Q.; Zhao, L.; Wang, X.; Wang, J. Penetration of particle through building envelope and its influence factor Build. Sci 2015 , 31 , 72–76 187. Tung, T.C.W.; Chao, C.Y.H.; John, B. A methodology to investigate the particulate penetration coefficient through building shell Atmos. Environ 1993 , 33 , 881–893. [ CrossRef ] 188. Thatcher, T.L.; Lunden, M.M.; Revzan, K.L.; Sextro, R.G.; Brown, N.J. A concentration rebound method for measuring particle penetration and deposition in the indoor environment Aerosol Sci. Technol 2003 , 37 , 847–864. [ CrossRef ] 189. Lai, A.C.K.; Fung, J.L.S.; Li, M.; Leung, K.Y. Penetration of fine particles through rough cracks Atmos. Environ 2012 , 60 , 436–443. [ CrossRef ] 190. Liu, D.L.; Nazaroff, W.W. Modeling pollutant penetration across building envelopes Atmos. Environ 2001 , 35 , 4451–4462. [ CrossRef ] 191. Tian, L.; Zhang, G.; Lin, Y.; Yu, J.; Zhou, J.; Zhang, Q. Mathematical model of particle penetration through smooth/rough building envelop leakages Build. Environ 2009 , 44 , 1144–1149. [ CrossRef ] 192. Tian, L.; Zhang, G.; Yu, J. Mathematical Simulation of Particle Penetration through Smooth and Rough Building Envelop Leakage J. Hunan Univ. Nat. Sci. Ed 2008 , 35 , 12–15 193. Li, A.; Ren, T.; Yang, C.; Lv, W.; Zhangm, F. Study on particle penetration through straight, L, Z and wedge-shaped cracks in buildings Build. Environ 2017 , 114 , 333–343. [ CrossRef ]

[Find the meaning and references behind the names: Yong, Basel, Bian, Chung, Dual, Lett, Nano]

Int. J. Environ. Res. Public Health 2018 , 15 , 438 29 of 29 194. Gao, Z.; Wang, X. Correlation study on air change rate and particle penetration coefficient through building envelope Heat. Vent. Air Cond 2016 , 46 , 14–19 195. Cao, G.; Xie, H.; Zhao, S. Strategic Research of Pollution Control of Indoor PM 2.5 in Public Buildings Build. Sci 2015 , 31 . [ CrossRef ] 196. Tu, Y.; Tu, G. Study on particulate matter (PM 2.5 ) filtration efficiency of ventilating air filters Heat. Vent. Air Cond 2016 , 46 , 49–54 197. Wang, X. The PM 2.5 Filtration Performance and Comprehensive Assessment of Air Filter Used in Fresh Air Unit. Ph.D. Thesis, Chongqing University, Chongqing, China, 2016 198. Lv, X.; Zhang, L.; Liu, Z.; Xu, X. Primary return air system PM 2.5 control strategy J. Environ. Eng 2016 , 10 , 7141–7146. [ CrossRef ] 199. Zhao, X.; Li, Y.; Hua, T.; Jiang, P.; Yin, X.; Yu, J.; Ding, B. Low-Resistance Dual-Purpose Air Filter Releasing Negative Ions and Effectively Capturing PM 2.5 ACS Appl. Mater. Interfaces 2017 , 9 , 12054–12063. [ CrossRef ] [ PubMed ] 200. Zhang, R.; Liu, C.; Hsu, P.C.; Zhang, C.; Liu, N.; Lee, H.R.; Lu, Y.; Qiu, Y.; Chu, S.; Cui, Y. Nanofiber Air Filters with High-Temperature Stability for Efficient PM 2.5 Removal from the Pollution Sources Nano Lett 2016 , 16 , 3642–3649. [ CrossRef ] [ PubMed ] 201. Li, M.; Feng, Y.; Wang, K.; Yong, W.F.; Yu, L.; Chung, T.S. Novel Hollow Fiber Air Filters for the Removal of Ultrafine Particles in PM 2.5 with Repetitive Usage Capability Environ. Sci. Technol 2017 , 51 , 10041–10049 [ CrossRef ] [ PubMed ] 202. Liu, C.; Hsu, P.C.; Lee, H.W.; Ye, M.; Zheng, G.; Liu, N.; Li, W.; Cui, Y. Transparent air filter for high-efficiency PM 2.5 capture Nat. Commun 2015 , 6 . [ CrossRef ] [ PubMed ] 203. Zhao, X.; Wang, S.; Yin, X.; Yu, J.; Ding, B. Slip-Effect Functional Air Filter for Efficient Purification of PM 2.5 Sci. Rep 2016 , 6 , 35472. [ CrossRef ] [ PubMed ] 204. Khalid, B.; Bai, X.; Wei, H.; Huang, Y.; Wu, H.; Cui, Y. Direct Blow-Spinning of Nanofibers on a Window Screen for Highly Efficient PM 2.5 Removal Nano Lett 2017 , 17 , 1140–1148. [ CrossRef ] [ PubMed ] 205. Shi, S.; Bian, Y.; Zhang, L.; Chen, C. A method for assessing the performance of nanofiber films coated on window screens in reducing residential exposures to PM 2.5 of outdoor origin in Beijing Indoor Air 2017 , 27 , 1190–1200. [ CrossRef ] [ PubMed ] © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).