Spatial Variation Of PM2 Research Articles

Source Apportionment of PM2.5 Based on Hybrid Chemical Transport and Receptor Model in Chongqing

In order to further improve the accuracy of fine particulate matter (PM2.5) source apportionment results, a hybrid source apportionment approach (CTM-RM) combining the capabilities of a receptor model (RM) and chemical transport model (CTM) was developed. The CTM-RM method was evaluated and applied according to a typical PM2.5 pollution process from January 21 to 27, 2019 in Chongqing. The average value of square prediction error based on CTM-RM was 84.58% lower than that of CAMx/PSAT during the campaign. Compared with that of CAMx/PSAT, the fractional error of PM2.5 and its chemical component concentrations decreased by 15.69%-92.86%. Furthermore, the temporal and spatial variations in PM2.5 source impacts could be obtained using the CTM-RM method in Chongqing. The average adjustment factor (R) values were 1.39±0.38 (agriculture sources), 1.54±0.48 (industrial sources), 1.01±0.13 (power sources), 1.02±0.58 (residential sources), 0.86±0.59 (transportation sources), and 0.58±0.67 (other sources) in the main urban areas of Chongqing. Additionally, the cumulative distribution functions of R were found to be distinct among the six sources. The residential and industrial sources were the main sources of PM2.5, with contributions of 46.23% and 28.23%, respectively. In contrast to that of the other sources, the transportation source impacts of PM2.5 (8.62%) increased significantly from the clear period to pollution period (P<0.001), indicating that the increase in PM2.5 concentrations was mainly driven by vehicular emissions during the pollution period in the main urban areas of Chongqing. The fitting functions between the initial simulated concentrations and R values of each source in the main urban areas of Chongqing could be used to evaluate PM2.5 concentrations at 47 air quality monitoring stations in Chongqing, and the correlation between the refined simulated concentrations and measured concentration of PM2.5 was significant (r=0.82, P<0.001). Compared with that during the clear period, the increases in the percentages of industrial source impacts of PM2.5 in Northeast Chongqing and residential source impacts of PM2.5 in Southeast Chongqing were 17.20% and 9.15% higher, respectively, than that in other areas during the pollution period. By contrast, the increasing percentage of transportation source impacts of PM2.5 in the main urban areas of Chongqing (66.39%) and Western Chongqing (84.16%) from the clear period to the pollution period were higher than that in other areas. The results of CTM-RM on January 26 indicated that the residential source impacts in Northeast Chongqing (64.56%) were higher than those in other areas, and the industry source impacts of PM2.5 were primarily observed in the main urban areas of Chongqing and Western Chongqing, with contributions of 25.26% and 21.20%, respectively.

An Integrated Approach to Characterize Temporal–Spatial Variations in PM2.5 Concentrations at the Ground Level and Its Implication on Health Impact Assessments

Although PM2.5 concentrations measured by the governmental air quality monitoring station (AQMS) have been widely used for conducting exposure assessments, it might be not able to reflect the residents’ exposures, especially for those associated with ground emissions. The present study was conducted in a city area for 1 year. A mobile monitoring station (MMS) was established to measure the PM2.5 concentrations at the ground level. A significant linear relationship (R2 = 0.53) was found between the MMS-measured concentrations and the corresponding concentrations obtained from the AQMS (15 m above the ground level), and the former was ∼ 1.11 times (95% CI: 1.08-1.15) in magnitude higher than that of the latter. To characterize the spatial variation of the area, the MMS-measured values were further classified into three different regions. A consistent trend was found in the present study for all collected data as industry region≒urban region &amp;gt; harbor region. The aforementioned results clearly indicate that the residents’ ambient PM2.5 exposures do have spatial differences. Seven-year AQMS-measured concentrations (i.e., AQMS7-yr) were used to establish the long-term PM2.5 concentrations at the ground level (i.e., MMS7-yr) of the three different regions using the linear regression equations obtained from the MMS and AQMS. Health impact functions and local health data were used to quantify the PM2.5-attributable health burden for both AQMS7-yr and MMS7-yr, respectively. Results show that the former is ∼ 10.4% lower in magnitude than the latter in the estimated lung cancer death attributed fraction (AF). In particular, the decrease of unit PM2.5 (μg/m3) would lead to a 0.75 and 0.71% decrease in the estimated AF of lung cancer death for AQMS7-yr and MMS7-yr, respectively. As a result, directly using AQMS7-yr would lead to an underestimation of ∼ 1,000 lung cancer deaths annually in Taiwan in comparison with those using MMS7-yr. The aforementioned results clearly indicate the importance of characterizing ground-level exposures for assessing the health impact of residents, and the methodology developed by the present study would be helpful for solving the aforementioned problem.

Insights about the Sources of PM2.5 in an Urban Area from Measurements of a Low-Cost Sensor Network

PM2.5 measurements using a network of lost-cost sensors were conducted during 2017–2019 in the greater area of Patras, Greece. The average PM2.5 concentration in all sites during the study period was 9.4 μg m−3, varying from 6.2 μg m−3 in the background areas to 12.8 μg m−3 at the city center. The site with the peak PM2.5 levels was not located in an area with high traffic density but rather in a square with pedestrian-only zones and a high restaurant density. The highest PM2.5 concentrations were observed during the colder period (November–March) due to high emissions from residential wood burning for heating purposes. The measurements of the sensors were used to estimate the importance of regional and local PM2.5 sources. During the warm period, regional transport dominated, contributing approximately 80–85% of the PM2.5 in the city center; however, during the colder period, the local sources were responsible for approximately half the PM2.5. The network operated reliably during this multiyear study. Such measurements provide, at a very low cost, valuable insights not only about the temporal and spatial variability of PM2.5 in a city but also about its sources, including the role of regional transport.

Heterogeneity and the determinants of PM2.5 in the Yangtze River Economic Belt

Haze has reached epidemic levels in many Chinese cities in recent years. Few studies have explored the determinants and heterogeneity of PM2.5. This paper investigates the spatiotemporal characteristics of PM2.5 through spatial analytical methods based on aerosol optical depth data from the Yangtze River Economic Belt (YREB) between 2000 and 2017. Geographically weighted regression and geodetector models were applied to assess the heterogeneity of key factors influencing PM2.5. The results indicate that the annual concentrations of PM2.5 in the YREB were 23.49–37.37 μg/m3, with an initial increase and a later decrease. PM2.5 pollution showed a diagonal high spatial distribution pattern in the northeast and a low spatial distribution in the southwest, as well as a noticeable spatial convergence. The spatial variability of PM2.5 was enlarged, and its main fractal dimension was in the northeast-southwest direction. There were clear spatiotemporal variations in the impacts of natural and anthropogenic factors on PM2.5. Our findings contribute to a better understanding of the impact mechanisms of PM2.5 and the geographic factors that form persistent and highly polluted areas and imply that more specific coping strategies need to be implemented in various areas toward successful particulate pollution prevention and control.

Spatial variations of PM2.5 emissions and social welfare induced by clean heating transition: A gridded cost-benefit analysis

Clean heating (CH) is regard as a key strategy to address the serious air pollution problem in northern China. A “one-fit-all” CH strategy will harm social welfare and engender resistance, due to the differences in heating habits and residents' income between urban and rural areas. To solve this problem, a 5 km × 5 km gridded cost-benefit analysis model for CH is build. Taking the case city as an example, an improved CH solution integrating multiple heat sources is proposed. By utilizing ISH resources, which are low-cost and low-emission-intensity, the improved solution reduces heating-induced PM2.5 emissions to one-fifth, and achieves higher net social benefits than existing CH strategies. Moreover, the spatial distributions of PM2.5 emission reductions and indoor and outdoor health benefits induced by CH strategy, including coal-to-gas/electricity and the improved strategy we propose, are simulated; revealing that the spillover effects of pollutants make CH will somewhat inevitably bring about a transfer of social welfare from rural areas to urban areas, but this can be compensated by opposite direction subsidies.

Investigation on the difference of PM2.5 transport flux between the North China Plain and the Sichuan Basin

In this study, an advanced transport flux method based on the comprehensive stereoscopic observation verification was used to evaluate the contribution of transport to pollution in the North China Plain (NCP) and Sichuan Basin (SCB). The simulations were verified using fixed-point lidar and satellite observations, and the results demonstrated that the modeling flux calculation method was suitable for investigating the evolutionary trend of PM2.5 flux. The monthly inflow, outflow, and net flux indicated the intensive interactions and significant temporal and spatial variations of PM2.5 transport though the boundary segment of the city and region. In both autumn and winter, the Beijing-Tianjin-Hebei (BTH) region always played the role of “source” region, while Cheng-Yu (CY) region principally acted as a “convergence” region. In autumn and winter, the BTH region was controlled by a cold front weather system, and the pollutants were readily transported to the surrounding areas. However, the CY region was affected by the complex terrain, with small wind speeds near the ground and poor horizontal diffusion. The upper air was controlled by the northwestern winds behind the trough, and the air subsidence is substantial. The combined action of horizontal and vertical wind directions caused the accumulation of pollutants in the basin. Notably, one intrinsic transport pathway during two representative months was identified based on the investigation of cross-city and cross-region transport, namely the southwest–northeast (SW–NE) pathway in the BTH region. In the CY region, the northeastern transport pathway greatly influenced the cities in the basin. Additionally, through the analysis of the typical heavy pollution processes in the two regions, it was found that the southwest pathway of the BTH region and northeast pathway of the CY region in inter-city transport were significant factors before reaching the most severe stage of pollution.

Long-term evaluation of a low-cost air sensor network for monitoring indoor and outdoor air quality at the community scale

Given the growing interest in community air quality monitoring using low-cost sensors, 30 PurpleAir II sensors (12 outdoor and 18 indoor) were deployed in partnership with community members living adjacent to a major interstate freeway from December 2017- June 2019. Established quality assurance/quality control techniques for data processing were used and sensor data quality was evaluated by calculating data completeness and summarizing PM2.5 measurements. To evaluate outdoor sensor performance, correlation coefficients (r) and coefficients of divergence (CoD) were used to assess temporal and spatial variability of PM2.5 between sensors. PM2.5 concentrations were also compared to traffic levels to assess the sensors’ ability to detect traffic pollution. To evaluate indoor sensors, indoor/outdoor (I/O) ratios during resident-reported activities were calculated and compared, and a linear mixed-effects regression model was developed to quantify the impacts of ambient air quality, microclimatic factors, and indoor human activities on indoor PM2.5. In general, indoor sensors performed more reliably than outdoor sensors (completeness: 73% versus 54%). All outdoor sensors were highly temporally correlated (r > 0.98) and spatially homogeneous (CoD<0.06). The observed I/O ratios were consistent with existing literature, and the mixed-effects model explains >85% of the variation in indoor PM2.5 levels, indicating that indoor sensors detected PM2.5 from various sources. Overall, this study finds that community-maintained sensors can effectively monitor PM2.5, with main data quality concerns resulting from outdoor sensor data incompleteness.

Deployment of networked low-cost sensors and comparison to real-time stationary monitors in New Delhi

ABSTRACT Air quality is a global challenge issue, and many regions of the world, such as India, are experiencing daunting challenges. An important aspect is to identify and then control the emissions from major contributing sources. To advance this aspect, this paper describes an air quality network that has been set up in the National Capital Territory of Delhi (NCT-Delhi) to identify major contributing source categories in real-time. The various components include an innovative cloud-based dashboard to compile the data in real-time from a series of PM instruments (Beta Attenuation Monitors (BAM)) and a low-cost sensor network (22 APT- MAXIMA sensors). Furthermore, at one of the locations (urban site), three real-time chemical speciation monitors are installed to provide elemental speciation (30 elements), elemental carbon (EC), and organic carbon (OC) data. PM2.5 concentrations at different sites (urban, industrial, and background) were compared to the BAM measurements over an 8-month period from May 2019 to February 2020; spanning the summer, monsoon, autumn, and winter seasons in Delhi. The APT sensor measurements were well correlated to the BAM measurements, with R2 values ranging between 0.84 and 0.95 for all sites. This validated that the APT-MAXIMA low-cost sensors can be a useful tool for distributed monitoring of PM2.5 levels. The mean PM2.5 concentrations showed a trend with winter (Dec, Jan, Feb: 205.2 ± 95.1 µg m−3) and autumn (Oct, Nov: 171.7 ± 128.3 µg m−3) highs and summer (May, Jun: 64.6 ± 57.2 µg m−3) and monsoon (Jul, Aug, Sep: 27.6 ± 16.7 µg m−3) lows. The bivariate polar plot reveals high PM2.5 levels originated from local/regional combustion sources located east and south-west of the urban site, especially when high PM2.5 episodes are encountered during the festival season and other smog episodes. Implications: Low-cost sensors are useful for distributed monitoring under both low and high pollution conditions. A cloud-based dashboard system provided real-time, remote access to the data and in the visualization of air quality in the entire region. The real-time data availability on the cloud enabled establishing hot-spot regions of air pollution, spatial variation of PM2.5, real-time source apportionment, and health risk estimates to benefit both policy makers, and the general public.

Spatial variations in PM2.5 oxidative potential in Toronto and Montreal, Canada

BACKGROUND AND AIM: Particulate matter air pollution is a recognized threat to human health. Recently, measures of particulate air pollution have been developed that incorporate information on the effects that particles may have inside the human respiratory tract. Among these is particle oxidative potential (OP), which is a measure of the ability of PM to cause oxidative reactions. OP can be quantified using assays that measure the ability of PM to deplete antioxidants in a synthetic respiratory tract lining fluid model. An alternative approach considers the ability of particles to generate reactive oxygen species (ROS) in the respiratory tract, as estimated using a mathematical model and concentrations of transition metals. The aim of our study was to develop land-use regression models to characterize the spatial distribution of ROS-generating capacity of PM2.5 and two measures of OP. METHODS: We conducted large-scale spatial monitoring campaigns across Montreal and Toronto, Canada and developed land use regression models to predict the spatial distribution of ROS-generating capacity of PM2.5 and the ability of PM2.5 extracts to deplete the antioxidants ascorbate (OPAA) and glutathione (OPGSH). RESULTS:In Montreal, the best models explained 54% of variation in ROS, 45% in OPAA and 31% in OPGSH. In Toronto, models explained 63% of variation in ROS, 77% in OPAA, and 44% in OPGSH. Variables that were identified as predictors across multiple models included distance to PM2.5 and NOX emitting facilities, total traffic counts, and distance to highways. CONCLUSIONS:These results contribute to existing knowledge of within-city spatial variations in particle oxidative potential using an unprecedented number of sensors. Exposure surfaces generated by these models can be applied in future studies of the health impact of PM2.5. Future work could clarify the sources of the most harmful components of PM2.5 to human health to aid in targeted reductions of emissions. KEYWORDS: particulate matter, oxidative potential, land use regression

Assessment of the Meteorological Impact on Improved PM2.5 Air Quality Over North China During 2016–2019 Based on a Regional Joint Atmospheric Composition Reanalysis Data‐Set

AbstractIn the context of China's clean air policy, the meteorological impacts on improved particulate matter (PM2.5) air quality during 2016–2019 are investigated based on a four‐year high‐resolution atmospheric composition reanalysis data‐set, which has been produced by the Joint Data Assimilation System to resolve long‐term fine‐scale air quality variability over China. The reanalysis assimilates surface air quality observations using the Weather Research and Forecasting model coupled with Chemistry and an ensemble‐based assimilation algorithm, and simultaneous assimilations of meteorological observations, chemical initial conditions (ICs) and emissions are applied to help reduce the uncertainty in meteorology, ICs and the emissions inventory. Further, objective weather classification method is applied to quantitatively explore synoptic circulation pattern changes and associated PM2.5 variability over North China by using this unique reanalysis data‐set. PM2.5 reanalysis data are also investigated according to different circulation types, and results indicate that temporal and spatial variations of PM2.5 are found to be closely connected with weather and circulation patterns. The northerly types correspond to the lower PM2.5 levels, while the southerly and easterly types correspond to the higher PM2.5 concentration due to favorable local meteorological conditions. According to the quantitative evaluation on circulation pattern changes, meteorological contribution have played a positive role in improving air quality in the context of China's clean air policy during 2016–2019. This study serves as a basis for future retrospective assessments of air pollutant variation and emissions regulation measures.

Spatial Variability of Finer Particulate Matter (PM&lt;SUB&gt;2.5&lt;/SUB&gt;) Mass Concentrations over the Jammu Urban Area

Atmospheric fine aerosols (PM2.5) have become a major concern in the context of rapidly deteriorating urban air quality by significantly affecting air quality, visibility, human health, and the earth’s climate. In this work, PM2.5 aerosol samples were collected during the summer (May-June) season at three different sites of Jammu urban area, Jammu and Kashmir. Significant site-specific spatial variation in PM2.5 mass concentrations was observed. The highest mass concentration of PM2.5 was observed at Site-2, a commercial hub with an average concentration 89.8 μg/m3 ± 34.7, and at Site-3, an Institutional area having an average PM2.5 concentration 67.9 μg/m3 ± 28.7 while the lowest concentrations were reported at Site-1, a residential area having an average concentration of 49.4 μg/m3 ± 22.7. Besides, 22.2%, 77.8%, and 44.4% of the samples at Site-1, Site-2, and Site-3, respectively exceeded National Ambient Air Quality Standards (NAAQS) of 60 μg/m3 prescribed by CPCB. The high PM2.5 in the commercial area can be attributed to higher vehicular movement and commercial activities. Site-3, University of Jammu, is an Institutional area where high concentrations of PM2.5 are possibly due to high vehicular movement in the campus and the use of generators for power backup.

Characteristics of PM2.5 spatial distribution and influencing meteorological conditions in Sichuan Basin, southwestern China

Understanding the mechanisms causing spatial variations of PM2.5 can provide implications of factors influencing air pollution possibilities. In this study, the spatial distribution of PM2.5 concentration was decomposed into various patterns by applying the Empirical Orthogonal Function (EOF) analysis to investigate main PM2.5 mass distribution patterns and corresponding meteorological conditions in the Sichuan Basin, southwestern China. The first five EOF patterns were determined and accounted for 80.5% of the total variance. The first pattern (61.7%) was consistent with the average PM2.5 mass distribution in the study area and was attributed to anthropogenic emissions. The remaining four patterns were ascribed to meteorological conditions and topography. The distribution patterns of the second and third EOFs were mainly caused by horizontal transport of pollutants accompanied by different circulation types. The fourth EOF was influenced by horizontal circulation, vertical movement and topography synthetically. The fifth EOF presented circular distribution of PM2.5 and resulted from convection activities accompanied with subsidence in the perimeter within the basin. This information provides a new perspective to explore the meteorological influence on regional distribution of PM2.5 in the Sichuan Basin, while having implications for refining pollution control strategies on a regional scale.

The Impact of Individual Mobility on Long-Term Exposure to Ambient PM2.5: Assessing Effect Modification by Travel Patterns and Spatial Variability of PM2.5.

The impact of individuals’ mobility on the degree of error in estimates of exposure to ambient PM concentrations is increasingly reported in the literature. However, the degree to which accounting for mobility reduces error likely varies as a function of two related factors—individuals’ routine travel patterns and the local variations of air pollution fields. We investigated whether individuals’ routine travel patterns moderate the impact of mobility on individual long-term exposure assessment. Here, we have used real-world time–activity data collected from 2013 participants in Erie/Niagara counties, New York, USA, matched with daily PM predictions obtained from two spatial exposure models. We further examined the role of the spatiotemporal representation of ambient PM as a second moderator in the relationship between an individual’s mobility and the exposure measurement error using a random effect model. We found that the effect of mobility on the long-term exposure estimates was significant, but that this effect was modified by individuals’ routine travel patterns. Further, this effect modification was pronounced when the local variations of ambient PM concentrations were captured from multiple sources of air pollution data (‘a multi-sourced exposure model’). In contrast, the mobility effect and its modification were not detected when ambient PM concentration was estimated solely from sparse monitoring data (‘a single-sourced exposure model’). This study showed that there was a significant association between individuals’ mobility and the long-term exposure measurement error. However, the effect could be modified by individuals’ routine travel patterns and the error-prone representation of spatiotemporal variability of PM.

Temporal and spatial variation of PM2.5 in indoor air monitored by low-cost sensors

Indoor air pollution has significant adverse health impacts, but its spatiotemporal variations and source contributions are not well quantified. In this study, we used low-cost sensors to measure PM2.5 concentrations in a typical apartment in Beijing. The measurements were conducted at 15 indoor sites and one outdoor site on 1-minute temporal resolution (convert to 10-minute averages for data analysis) from March 14 to 24, 2020. Based on these highly spatially-and temporally-resolved data, we characterized spatiotemporal variations and source contributions of indoor PM2.5 in this apartment. It was found that indoor particulate matter predominantly originates from outdoor infiltration and cooking emissions with the latter contributing more fine particles. Indoor PM2.5 concentrations were found to be correlated with ambient levels but were generally lower than those outdoors with an average I/O of 0.85. The predominant indoor source was cooking, leading to occasional high spikes. The variations observed in most rooms lagged behind those measured outdoors and in the studied kitchen. Differences between rooms were found to depend on pathway distances from sources. On average, outdoor sources contributed 36% of indoor PM2.5, varying extensively over time and among rooms. From observed PM2.5 concentrations at the indoor sites, source strengths, and pathway distances, a multivariate regression model was developed to predict spatiotemporal variations of PM2.5. The model explains 79% of the observed variation and can be used to dynamically simulate PM2.5 concentrations at any site indoors. The model's simplicity suggests the potential for regional-scale application for indoor air quality modeling.

Temporal and Spatial Variation of PM2.5 in Xining, Northeast of the Qinghai–Xizang (Tibet) Plateau

PM2.5 was sampled from January 2017 to May 2018 at an urban, suburban, industrial, and rural sites in Xining. The annual mean of PM2.5 was highest at the urban site and lowest at the rural site, with an average of 51.5 ± 48.9 and 26.4 ± 17.8 μg·m−3, respectively. The average PM2.5 concentration of the industrial and suburban sites was 42.8 ± 27.4 and 37.2 ± 23.7 μg·m−3, respectively. All sites except for the rural had concentrations above the ambient air quality standards of China (GB3095-2012). The highest concentration of PM2.5 at all sites was observed in winter, followed by spring, autumn, and summer. The concentration of major constituents showed statistically significant seasonal and spatial variation. The highest concentrations of organic carbon (OC), elemental carbon (EC), water-soluble organic carbon (WSOC), and water-soluble inorganic ions (WSIIs) were found at the urban site in winter. The average concentration of F− was higher than that in many studies, especially at the industrial site where the annual average concentration of F− was 1.5 ± 1.7 μg·m−3. The range of sulfur oxidation ratio (SOR) was 0.1–0.18 and nitrogen oxidation ratio (NOR) was 0.02–0.1 in Xining. The higher SO42−/NO3− indicates that coal combustion has greater impact than vehicle emissions. The results of the potential source contribution function (PSCF) suggest that air mass from middle- and large-scale transport from the western areas of Xining have contributed to the higher level of PM2.5. On the basis of the positive matrix factorization (PMF) model, it was found that aerosols from salt lakes and dust were the main sources of PM2.5 in Xining, accounting for 26.3% of aerosol total mass. During the sandstorms, the concentration of PM2.5 increased sharply, and the concentrations of Na+, Ca2+ and Mg2+ were 1.13–2.70, 1.68–4.41, and 1.15–5.12 times higher, respectively, than annual average concentration, implying that aerosols were mainly from dust and the largest saltwater lake, Qinghai Lake, and many other salt lakes in the province of Qinghai. Time-of-flight secondary ion mass spectrometry (ToF-SIMS) was utilized to study the surface components of PM2.5 and F− was found to be increasingly distributed from the surface to inside the particles. We determined that the extremely high PM2.5 concentration appears to be due to an episode of heavy pollution resulting from the combination of sandstorms and the burning of fireworks.

Affinity zone identification approach for joint control of PM2.5 pollution over China

In recent years, the Chinese government has made great efforts to jointly control and prevent air pollution, especially fine particulate matter (PM2.5). However, these efforts are challenged by technical constraints due to the significant temporal and spatial heterogeneity of PM2.5 across China. In this study, the Affinity Zone Identification Approach (AZIA), which combines rotated principal component analysis (RPCA) with revised clustering analysis, was developed and employed to regionalize PM2.5 pollution in China based on data from 1496 air quality monitoring sites recorded from 2013 to 2017. Two clustering methods, cluster analysis with statistical test (CAST) and K-center-point (K-medoids) clustering, were compared and revised to eliminate unspecified sites. Site zonation was finally extended to the municipality scale for the convenience of the controlling measures. The results revealed that 17 affinity zones with 5 different labels from clean to heavily polluted areas could be identified in China. The heavily polluted areas were mainly located in central and eastern China as well as Xinjiang Province, with regional average annual PM2.5 concentrations higher than 66 μg/m3. The new approach provided more comprehensive and detailed affinity zones than obtained in a previous study (Wang et al., 2015b). The North China Plain and Northeastern China were both further divided into northern and southern parts based on different pollution levels. In addition, five affinity zones were first recognized in western China. The findings provide not only a theoretical basis to further display the temporal and spatial variations in PM2.5 but also an effective solution for the cooperative control of air pollution in China.

SPATIO-TEMPORAL VARIATION OF PM2.5 CONCENTRATION IN CHINA FROM 1998 TO 2016 AND ITS RESPONSE TO ECONOMIC DEVELOPMENT

Abstract. China's economy has experienced rapid development in the past few decades, and economic development has also brought serious pollution problems, which has attracted wide attention of scholars at home and abroad. Based on the data of global PM2.5 remote sensing products and China's economic development from 1998 to 2016, the temporal and spatial variations of PM2.5 concentration in China from 1998 to 2016 were analyzed, and the response of PM2.5 concentration in China to economic development was studied. The results showed that the average annual PM2.5 concentration in 1998–2016 showed the spatial distribution characteristics of high in the East and low in the west; during 1998–2016, PM2.5 increased significantly in most regions, but decreased significantly in Inner Mongolia, Shaanxi, Ningxia and Gansu, while PM2.5 did not change significantly in some parts of the central region; during 1998–2007, PM2.5 concentration in most regions of China experienced rapid economic development. The concentration of PM2.5 in a few areas such as Inner Mongolia decreased significantly, while that in Yunnan, Sichuan and Inner Mongolia did not change significantly. During the 10 years of economic slowdown in China (2008–2016), the downward trend of PM2.5 concentration in China was expanding. The concentration of PM2.5 in the central and southern regions decreased or did not change significantly, except in the northwest and a few northeast regions. The change of PM2.5 concentration responds obviously to economic development, but the response of different regional economic development to the change of PM2.5 concentration is different.

Assessing spatial variation of PM2.5 and NO2 across Europe using Geographically Weighted Regression

Assessing spatial variation of PM2.5 and NO2 across Europe using Geographically Weighted Regression

Insights into the temporal and spatial characteristics of PM2.5 transport flux across the district, city and region in the North China Plain

The Beijing–Tianjin–Hebei (BTH) region in the North China Plain (NCP) is significantly affected by inter–district, city and region transport, but systematic works on this aspect using flux calculation method remain insufficient. To obtain further insights into the temporal and spatial characteristics of PM2.5 flux, the Weather Research and Forecasting (WRF) model and the Comprehensive Air Quality Model with Extensions (CAMx) have been applied during January, April, July and October 2016. The results demonstrated that the modeling flux calculation method was suitable for investigating the evolutionary trend of PM2.5 flux by comparing cross–district simulations and observations. The total of monthly inflow, outflow and net flux indicated the intensive interactions and prominent temporal and spatial variations of PM2.5 transport though the boundary segment of the district, city and region. The vertical distribution analysis of net flux shows that PM2.5 net flux between target megacity/region and surroundings differed with the altitude, and the altitude at which its maximum intensity was closely related to neighboring areas and occurrence of month. Notably, three main transport pathways during four months have been identified based on the investigation of cross–city transport, namely the northwest–southeast (NW–SE) for January and April, the southeast–northwest (SE–NW) for July and October, the southwest–northeast (SW–NE) for all months, which were further confirmed by cross–region transport. Additionally, summer and winter monsoon have significant influences on the SE–NW and NW–SE pathway, respectively. Furthermore, this research draws upon a typical process of PM2.5 haze episodes from 11 to 17 October in Beijing, Tangshan and Shijiazhuang, with the aim of demonstrating flux intensity variations in different stages of haze episodes. Overall, the evolution of flux intensity at the higher–altitude layer have similar pattern to that of lower–altitude layer, in which the former was approximately 2 times the latter. Furthermore, inter–city transport along the SW–NE pathway played a crucial role before reaching the most severe stage of pollution, while the local emissions were more conductive to forming heavy pollution extremes. Overall, flux intensity calculations provide scientific support to put forward effective joint control measures and acquire a better understanding of evolutionary mechanism of haze episodes in the NCP.

Exploring the Spatial Variation Characteristics and Influencing Factors of PM2.5 Pollution in China: Evidence from 289 Chinese Cities

Haze pollution has become an urgent environmental problem due to its impact on the environment as well as human health. PM2.5 is one of the core pollutants which cause haze pollution in China. Existing studies have rarely taken a comprehensive view of natural environmental conditions and socio-economic factors to figure out the cause and diffusion mechanism of PM2.5 pollution. This paper selected both natural environmental conditions (precipitation (PRE), wind speed (WIN), and terrain relief (TR)) and socio-economic factors (human activity intensity of land surface (HAILS), the secondary industry's proportion (SEC), and the total particulate matter emissions of motor vehicles (VE)) to analyze the effects on the spatial variation of PM2.5 concentrations. Based on the spatial panel data of 289 cities in China in 2015, we used spatial statistical methods to visually describe the spatial distribution characteristics of PM2.5 pollution; secondly, the spatial agglomeration state of PM2.5 pollution was characterized by Moran’s I; finally, several regression models were used to quantitatively analyze the correlation between PM2.5 pollution and the selected explanatory variables. Results from this paper confirm that in 2015, most cities in China suffered from severe PM2.5 pollution, and only 17.6% of the sample cities were up to standard. The spatial agglomeration characteristics of PM2.5 pollution in China were particularly significant in the Beijing–Tianjin–Hebei region. Results from the global regression models suggest that WIN exerts the most significant effects on decreasing PM2.5 concentration (p &lt; 0.01), while VE is the most critical driver of increasing PM2.5 concentration (p &lt; 0.01). Results from the local regression model show reliable evidence that the relation between PM2.5 concentrations and the explanatory variables varied differently over space. VE is the most critical factor that influences PM2.5 concentrations, which means controlling motor vehicle pollutant emissions is an effective measure to reduce PM2.5 pollution in Chinese cities.