Spatial Patterns Of PM2 Research Articles

Modeling the Effects of Drivers on PM2.5 in the Yangtze River Delta with Geographically Weighted Random Forest

Establishing an efficient PM2.5 prediction model and in-depth knowledge of the relationship between the predictors and PM2.5 in the model are of great significance for preventing and controlling PM2.5 pollution and policy formulation in the Yangtze River Delta (YRD) where there is serious air pollution. In this study, the spatial pattern of PM2.5 concentration in the YRD during 2003–2019 was analyzed by Hot Spot Analysis. We employed five algorithms to train, verify, and test 17 years of data in the YRD, and we explored the drivers of PM2.5 exposure. Our key results demonstrated: (1) High PM2.5 pollution in the YRD was concentrated in the western and northwestern regions and remained stable for 17 years. Compared to 2003, PM2.5 increased by 10–20% in the southeast, southwest, and western regions in 2019. The hot spot for percentage change of PM2.5 was mostly located in the southwest and southeast regions in 2019, while the interannual change showed a changeable spatial distribution pattern. (2) Geographically Weighted Random Forest (GWRF) has great advantages in predicting the presence of PM2.5 in comparison with other models. GWRF not only improves the performance of RF, but also spatializes the interpretation of variables. (3) Climate and human activities are the most important drivers of PM2.5 concentration. Drought, temperature, and temperature difference are the most critical and potentially threatening climatic factors for the increase and expansion of PM2.5 in the YRD. With the warming and drying trend worldwide, this finding can help policymakers better consider these factors for PM2.5 prediction. Moreover, the effect of interference from humans on ecosystems will increase again after COVID-19, leading to a rise in PM2.5 concentration. The strong explanatory power of comprehensive ecological indicators for the distribution of PM2.5 will be a crucial indicator worthy of consideration by decision-making departments.

Spatio-Temporal Change Pattern Investigation of PM2.5 in Jiangsu Province with MODIS Time Series Products

In the last decade, the spatio-temporal patterns of PM2.5 on various scales, ranging from global, continent, and country to regional levels, has been the focus of considerable studies. However, these studies on spatio-temporal variability have concentrated primarily on changes in the spatial distribution patterns of regional PM2.5 concentrations and ignored temporal characteristics at a local site from a heterogeneous surface, such as local variability, persistence, and stability of PM2.5 exposure. Understanding the temporal characteristics of PM2.5 concentration changes at the local scale will help determine the local impacts of PM2.5, such as local exposure risk and vulnerability to PM2.5. This study aims to reveal the local characteristics of temporal variation at the scale of a prefecture-level city and its distinct-varying patterns from those at the provincial scale by using the annual satellite-derived PM2.5 concentration product from 2000 to 2015. The evolutionary trends, stability, and persistence of annual changes were discovered with a set of time series analysis methods, such as linear regression analysis + F-test, coefficient of variation method, and Hurst index. This study uses PM2.5 product data for a total of 16 years, from 2000 to 2015, and uses time series analysis methods, such as Theil–Sen median trend analysis + Mann–Kendall test, one-dimensional linear regression analysis + F-test, coefficient of variation method, and Hurst index, to reveal the temporal variation characteristics and spatial patterns of PM2.5 in Jiangsu Province. The results show that the increasing trends or slopes of annual averaged PM2.5 concentrations in Jiangsu Province are not consistent at the prefecture-level city scale, but they are consistent in northern, central and southern Jiangsu at a larger upward trend since 2000. The areas with significant increasing trends are concentrated in Xuzhou and Lianyungang and other northern cities. From the viewpoint of variability, the areas in medium and high variability are mainly aggregated in the areas north of the Yangtze River. According to the combination of persistence analysis and trend analysis, future variation in PM2.5 concentrations indicates an inverse persistence for an increasing trend, meaning the air quality decline in Jiangsu will slow.

The spatial heterogeneity of the impact of PM2.5 on domestic tourism flows in China.

As haze pollution intensifies, its impact on tourism is becoming increasingly obvious. However, limited studies have analyzed the impacts of haze pollution on tourism. To explore the contribution rate and impact of PM2.5 pollution on tourism flows, panel data on 341 prefecture-level cities in China from 2001 to 2015 were used. The results illustrated that the changes in PM2.5 pollution and domestic tourism flows showed a similar partial-most anti-phase main spatial pattern in space, as well as other spatial patterns of PM2.5. From a regional perspective, the contribution rate of PM2.5 to domestic tourism flows was less than that of traditional factors, such as GDP, GDP_500, and 45A, but larger than that of the Airport factor. The contribution rate of the interaction between PM2.5 and 45A on domestic tourism flows was the largest. From a local perspective, PM2.5 pollution had a negative impact on domestic tourism flows in northern China, while it had a positive impact in other regions. The classic environmental Kuznets curve (EKC) hypothesis showed applicability to the Chinese tourism industry, and the is of great significance for comprehensively understanding the impact of PM2.5 pollution on tourism flows and for promoting the sustainable development of domestic tourism.

Spatiotemporal Distribution Patterns and Exposure Risks of PM2.5 Pollution in China

The serious pollution of PM2.5 caused by rapid urbanization in recent years has become an urgent problem to be solved in China. Annual and daily satellite-derived PM2.5 datasets from 2001 to 2020 were used to analyze the temporal and spatial patterns of PM2.5 in China. The regional and population exposure risks of the nation and of urban agglomerations were evaluated by exceedance frequency and population weight. The results indicated that the PM2.5 concentrations of urban agglomerations decreased sharply from 2014 to 2020. The region with PM2.5 concentrations less than 35 μg·m−3 accounted for 80.27% in China, and the average PM2.5 concentrations in 8 urban agglomerations were less than 35 μg·m−3 in 2020. The spatial distribution pattern of PM2.5 concentrations in China revealed higher concentrations to the east of the Hu Line and lower concentrations to the west. The annual regional exposure risk (RER) in China was at a high level, with a national average of 0.75, while the average of 14 urban agglomerations was as high as 0.86. Among the 14 urban agglomerations, the average annual RER was the highest in the Shandong Peninsula (0.99) and lowest in the Northern Tianshan Mountains (0.76). The RER in China has obvious seasonality; the most serious was in winter, and the least serious was in summer. The population exposure risk (PER) east of the Hu Line was significantly higher than that west of the Hu Line. The average PER was the highest in Beijing-Tianjin-Hebei (4.09) and lowest in the Northern Tianshan Mountains (0.71). The analysis of air pollution patterns and exposure risks in China and urban agglomerations in this study could provide scientific guidance for cities seeking to alleviate air pollution and prevent residents’ exposure risks.

Heterogeneous Graphical Model for Non-Negative and Non-Gaussian PM2.5 data

AbstractStudies on the conditional relationships between PM2.5 concentrations among different regions are of great interest for the joint prevention and control of air pollution. Because of seasonal changes in atmospheric conditions, spatial patterns of PM2.5 may differ throughout the year. Additionally, concentration data are both non-negative and non-Gaussian. These data features pose significant challenges to existing methods. This study proposes a heterogeneous graphical model for non-negative and non-Gaussian data via the score matching loss. The proposed method simultaneously clusters multiple datasets and estimates a graph for variables with complex properties in each cluster. Furthermore, our model involves a network that indicate similarity among datasets, and this network can have additional applications. In simulation studies, the proposed method outperforms competing alternatives in both clustering and edge identification. We also analyse the PM2.5 concentrations' spatial correlations in Taiwan's regions using data obtained in year 2019 from 67 air-quality monitoring stations. The 12 months are clustered into four groups: January–March, April, May–September and October–December, and the corresponding graphs have 153, 57, 86 and 167 edges respectively. The results show obvious seasonality, which is consistent with the meteorological literature. Geographically, the PM2.5 concentrations of north and south Taiwan regions correlate more respectively. These results can provide valuable information for developing joint air-quality control strategies.

Effects of the COVID-19 shutdown on spatial and temporal patterns of air pollution in New York City

Using spatially- and temporally-resolved data from the New York City Community Air Survey (NYCCAS) and the New York State (NYS) Department of Environmental Conservation (DEC) network, we characterized changes in fine particulate matter (PM2.5) and nitrogen dioxide (NO2) following the COVID-19 shutdown in NYC (3/20/20 – 6/7/20). Difference-in-difference analysis of PM2.5 and NO2 measured at 93 sites were used to estimate the change in citywide pollution attributable to the shutdown. We also quantified how these pollutant changes varied among different demographic groups using difference-in-difference analyses stratified by neighborhood poverty levels and rates of PM2.5-attributable health outcomes. Spatial patterns of PM2.5 and NO2 were interpolated across NYC by fitting land-use regression models to measurements at the 93 sites. Weather conditions and emission source trends were analyzed to determine the potential effects of meteorology and specific emission sources on the observed pollution changes. We estimate that citywide average PM2.5 and NO2 decreased by approximately 25% and 29%, respectively, due to the shutdown. Weather readings show little evidence that meteorology biased our results in the direction of our findings. Data on major sources of PM2.5 and NO2 pollution suggests that decreased vehicle traffic and commercial cooking contributed to declines in air pollution during this period. Pollution reductions occurred disproportionately in the city's central business district (CBD), with smaller changes in other areas of the city, such as those with the highest burden of air pollution-related health impacts. These findings emphasize the need to target pollution sources in communities that suffer the greatest from pollution exposure in the design of equitable environmental health policy.

Multiscale and multisource data fusion for full-coverage PM2.5 concentration mapping: Can spatial pattern recognition come with modeling accuracy?

In spite of a variety of PM2.5 modeling schemes, general guidance for full-coverage PM2.5 concentration mapping from satellite observations is still lacking. The current technical gap is tied to how to integrate multiscale data from multiple sources to generate a spatially contiguous map that can better recognize PM2.5 distribution patterns without compromising modeling accuracy. In this study, ten different PM2.5 concentration data sets were generated using distinct mapping strategies and compared to one another, aiming to facilitate full-coverage PM2.5 concentration mapping with a generalized approach. The inter-comparison results indicated that different mapping strategies could yield comparable modeling accuracy albeit distinct PM2.5 distributions over space. Although the inclusion of PM2.5 autocorrelation terms as predictors can markedly improve the modeling accuracy, spatial patterns of PM2.5 estimations could be apparently distorted under different parameter configurations. In an attempt to balance the conflicting objectives, the optimal PM2.5 mapping scheme was proposed for broadened applications. A daily full-coverage PM2.5 concentration data set with 5-km resolution in China between 2015 and 2020 was generated for a demonstration to infer an apparent decreasing trend of PM2.5 across China over the past five years. Besides, the examination of COVID-19 pandemic impacts on regional air quality variations reveals a pattern of marked PM2.5 concentration decrease that cannot be easily realized by site-based air quality measurements. It is indicative that the proposed approach in this study can offer an optimal framework in support of various full-coverage PM2.5 mapping practices.

Effects of the COVID-19 shutdown on spatial and temporal patterns of air pollution in New York City

BACKGROUND AND AIM: To mitigate the COVID-19 pandemic, many governments across the world implemented “lockdown” measures. The resulting changes in economic activity had effects that cascaded across numerous aspects of everyday life. Using data from the New York City Community Air Survey (NYCCAS) and from the New York State (NYS) Department of Environmental Conservation (DEC), we characterized changes in fine particulate matter (PM2.5) and nitrogen dioxide (NO2) following the COVID-19 shutdown in New York City (NYC). METHODS: Difference-in-difference analysis of PM2.5 and NO2 measured at the DEC sites was used to calculate the change in citywide daily average pollutant concentrations that could be attributed to the COVID-19 shutdown. Spatial patterns of PM2.5 and NO2 in the winter and spring seasons of 2019 and 2020 were interpolated across NYC by fitting generalized additive models to measurements at 82-93 NYCCAS sites. Weather conditions and emission source trends were analyzed to determine the potential effects of meteorology and specific emission sources on the observed pollution changes. RESULTS:Difference-in-difference analysis suggests that PM2.5 and NO2 decreased by approximately 20% and 27%, respectively, because of the COVID-19 shutdown. Weather readings at LaGuardia Airport in NYC show little evidence that meteorology played a major role in observed pollutant decreases. Both PM2.5 and NO2 exhibited decreased spatial variability in NYC during the COVID-19 shutdown. Widespread increases in daytime (6am-8pm) traffic speeds and fewer open small businesses following the shutdown suggest that decreased vehicle traffic and business activity contributed to declines in air pollution during this period. CONCLUSIONS:Given the devastating effect the pandemic has had on lives and livelihoods, these pollution reductions are not truly cause for celebration. However, this study shows the potential for regulations on specific emission sources, such as traffic and commercial cooking, to control citywide pollution in the future. KEYWORDS: Air pollution, Oxides of nitrogen, Particulate matter, Policy, Causal inference

Spatio-temporal variations in fine particulate matter and evaluation of associated health risk over Pakistan.

Human health and the environment are adversely affected by fine particulate matter. By utilizing standard deviation ellipse and trend analyses, we studied the spatial patterns and temporal trends of PM2.5 over Pakistan from 1998 to 2016. The outcomes of these analyses indicated that PM2.5 concentrations were considerably amplified in Pakistan, particularly in the provinces of Punjab and Sindh. The areal extent of PM2.5 concentrations below 15 μg/m3 declined constantly, and the area with PM2.5 concentrations above 35 μg/m3 increased significantly. The highly affected cities were Lahore, Faisalabad, Multan, Southern Gujranwala, Dera Ghazi Khan, Bahawalpur, Sukkur, and Larkana. Overall, the northwest-southeast axis experienced more rapid variations in the spatial pattern of PM2.5 than the northeast-southwest axis; similarly, the east-north axis also experienced faster changes in the spatial distribution of this crucial pollutant than the west-south axis. To support nationwide air pollution control, a two-tier level was recommended for allocated regions in Pakistan depending on their PM2.5 concentrations. From 1998 to 2016, health risks expanded and increased in Pakistan, particularly in Lahore, Karachi, Multan, Gujranwala, Faisalabad, and Hyderabad; these are Pakistan's most populated cities. The outcomes of this study suggest that human health is continuously affected by PM2.5 in Pakistan, and that a plan of action to combat air pollution is immediately needed. Integr Environ Assess Manag 2021;17:1243-1254. © 2021 SETAC.

Impacts of Industrial Restructuring and Technological Progress on PM2.5 Pollution: Evidence from Prefecture-Level Cities in China.

PM2.5 pollution has produced adverse effects all over the world, especially in fast-developing China. PM2.5 pollution in China is widespread and serious, which has aroused widespread concern of the government, the public and scholars. This paper evaluates the evolution trend and spatial pattern of PM2.5 pollution in China based on the data of 281 prefecture-level cities in China from 2007 to 2017, and reveals the pollution situation of PM2.5 and its relationship with industrial restructuring and technological progress by using spatial dynamic panel model. The results show that China’s PM2.5 pollution has significant path dependence and spatial correlation, and the industrial restructuring and technological progress have significant positive effects on alleviating PM2.5 pollution. As a decomposition item of technological progress, technical change effectively alleviates PM2.5 pollution. Another important discovery is that the interaction between industrial restructuring and technological progress will aggravate PM2.5 pollution. Finally, in order to effectively improve China’s air quality, while advocating the Chinese government to pursue high-quality development, this paper puts forward a regional joint prevention mechanism.

Interannual Characteristics of Fine Particulate Matter in North China and Its Relationship with Land Use and Land Cover Change

Fine particulate matter (PM2.5) is an important component of air pollution, and thus it is meaningful to analyze its influencing factors. According to existing literature, most studies to date have focused on the relationship between PM2.5 and meteorological or economic factors, whereas fewer have analyzed the relationship between PM2.5 and land use and land cover change (LUCC). This study employed spatial distribution data of PM2.5 and land use and land cover data to analyze the relationship between dynamic characteristics of PM2.5 and LUCC. A geographically weighted regression (GWR) model and spatial analysis tools based on ArcGIS were used to analyze the relationship between PM2.5 dynamic characteristics and LUCC. North China was selected as the study area, and the results showed that ① The spatial pattern of PM2.5 in North China was high in the southeast and low in the northwest for 18 years. From a time perspective, the PM2.5 reached its maximum value in 2006 and has maintained a high value since then. The PM2.5 exceeded the permissible standard in most of the cities, with serious environmental pollution generally. ② The main land use and land cover types in North China from 2000 to 2015 were cropland, woodland, and grassland, and the land use and land cover change showed a trend of great decline in cropland and a great increase in construction land. ③ The results of the GWR model showed that local R2 is low in non-LUCC areas and high in LUCC areas, and the PM2.5 dynamic characteristics have a significant response to LUCC. ④ For different land use and land cover types, the distribution of PM2.5 showed a trend of construction land > cropland > water area > grassland > woodland > unused land, for different types of LUCC. PM2.5 concentration increased when natural land changed to artificial land and decreased when artificial land changed to natural land.

Changes in spatial patterns of PM2.5 pollution in China 2000–2018: Impact of clean air policies

To improve air quality, China has been implementing strict clean air policies since 2013. These policies not only substantially improved air quality but may also modify the spatial distribution of air pollution, since urban emission sources were under stricter control and some were moved to rural regions with lower air quality improvement targets and lacking of monitoring. Here, we predicted satellite-based monthly PM2.5 concentrations during 2000–2018 at a 1-km resolution with complete spatial-temporal coverage to analyze changes in the spatial pattern of PM2.5 pollution in China. We found that the PM2.5 concentration in urban regions was higher than that in rural regions of the same city by an average of 3.3 μg/m3 during 2000–2018. This urban-rural disparity in PM2.5 concentration significantly increased from 2.5 μg/m3 in 2000 and peaked in 2007 of 3.8 μg/m3, then it sharply declined by 49% during 2013–2018 with the implementation of clean air policies. This shrinkage in the urban-rural PM2.5 gap was partly due to the 1.3 μg/m3 greater average decrease in the PM2.5 level in the urban region than in the rural region of the same town during 2013–2018 on average. We also observed that cities that started monitoring earlier experienced greater decreases in the urban-rural PM2.5 difference, and regions surrounding monitor showed significantly greater PM2.5 decrease than regions far away from monitor during 2013–2018. Additionally, clean air policies modified the relationship between PM2.5 concentrations and per capita gross domestic product (GDP), leading to a lower PM2.5 level with the same per capita GDP after 2013. Emissions in rural and suburban regions should be considered to further improve air quality in China.

Simulation of airborne trace metals in fine particulate matter over North America

Trace metal distributions are of relevance to understand sources of fine particulate matter (PM2.5), PM2.5-related health effects, and atmospheric chemistry. However, knowledge of trace metal distributions is lacking due to limited ground-based measurements and model simulations. This study develops a simulation of 12 trace metal concentrations (Si, Ca, Al, Fe, Ti, Mn, K, Mg, As, Cd, Ni and Pb) over continental North America for 2013 using the GEOS-Chem chemical transport model. Evaluation of modeled trace metal concentrations with observations indicates a spatial consistency within a factor of 2. The spatial distribution of trace metal concentrations reflects their primary emission sources. Crustal element (Si, Ca, Al, Fe, Ti, Mn, K) concentrations are enhanced over the central US from anthropogenic fugitive dust and over the southwestern U.S. due to natural mineral dust. Heavy metal (As, Cd, Ni and Pb) concentrations are high over the eastern U.S. from industry. K is abundant in the southeast from biomass burning. High concentrations of Mg are observed along the coast from sea spray. The spatial pattern of PM2.5 mass is most strongly correlated with Pb, Ni, As and K due to their signature emission sources. Challenges remain in accurately simulating observed trace metal concentrations. Halving anthropogenic fugitive dust emissions in the 2011 National Air Toxic Assessment (NATA) inventory and doubling natural dust emissions in the default GEOS-Chem simulation was necessary to reduce biases in crustal element concentrations. A fivefold increase of anthropogenic emissions of As and Pb was necessary in the NATA inventory to reduce the national-scale bias versus observations by more than 80%, potentially reflecting missing sources.

No one knows which city has the highest concentration of fine particulate matter

Exposure to ambient fine particulate matter (PM2.5) is the leading global environmental risk factor for mortality and disease burden, with associated annual global welfare costs of trillions of dollars. Examined within is the ability of current data to answer a basic question about PM2.5, namely the location of the city with the highest PM2.5 concentration. The ability to answer this basic question serves as an indicator of scientific progress to assess global human exposure to air pollution and as an important component of efforts to reduce its impacts. Despite the importance of PM2.5, we find that insufficient monitoring data exist to answer this basic question about the spatial pattern of PM2.5 at the global scale. Only 24 of 234 countries have more than 3 monitors per million inhabitants, while density is an order of magnitude lower in the vast majority of the world's countries, with 141 having no regular PM2.5 monitoring at all. The global mean population distance to nearest PM2.5 monitor is 220 km, too large for exposure assessment. Efforts to fill in monitoring gaps with estimates from satellite remote sensing, chemical transport modeling, and statistical models have biases at individual monitor locations that can exceed 50 μg m−3. Progress in advancing knowledge about the global distribution of PM2.5 will require a harmonized network that integrates different types of monitoring equipment (regulatory networks, low-cost monitors, satellite remote sensing, and research-grade instrumentation) with atmospheric and statistical models. Realization of such an integrated framework will facilitate accurate identification of the location of the city with the highest PM2.5 concentration and play a key role in tracking the progress of efforts to reduce the global impacts of air pollution.

Impact of Obesity and Ozone on the Association Between Particulate Air Pollution and Cardiovascular Disease and Stroke Mortality Among US Adults.

BackgroundCardiovascular diseases (CVDs) and stroke are the highest and third highest causes of death, respectively, in the whole United States. It is well established that both long‐ and short‐term exposure to particulate air pollution (particulate matter with diameters <2.5 μm [PM 2.5]) increases the risks of both CVD and stroke mortality.Methods and ResultsWe combined county‐level data for CVD and stroke mortality, and prevalence of hypertension and obesity, with spatial patterns of PM 2.5 and ozone in a cross‐sectional ecological study. We found significant positive associations between both CVD (β=15.4, P<0.001) and stroke (β=2.7, P<0.001) mortality with PM 2.5. Ozone had significant link with just CVD (β=1372.1, P<0.001). Once poverty, ethnicity, and education were taken into account, there were still significant positive associations between PM 2.5 and both CVD (β=1.2, P<0.001) and stroke (β=1.1, P<0.001) mortality. Moreover, the association between CVD and ozone remained after adjustment for these factors (β=21.8, P<0.001). PM 2.5 and ozone were independent risk factors. The impact of PM 2.5 on CVD and stroke mortality was strongly dependent on the prevalence of obesity. Hypertension partially mediated the associations of PM 2.5 and mortality from CVD and stroke.ConclusionsThere was a spatial association between PM 2.5 exposure and the leading causes of death and disability in United States. The effect of PM 2.5 was considerably greater in areas where obesity is more prevalent. Hypertension is a possible mediator of the association of PM 2.5 and both CVD and stroke.

A framework for delineating the regional boundaries of PM2.5 pollution: A case study of China.

Fine particulate matter (PM2.5) pollution has been a major issue in many countries. Considerable studies have demonstrated that PM2.5 pollution is a regional issue, but little research has been done to investigate the regional extent of PM2.5 pollution or to define areas in which PM2.5 pollutants interact. To allow for a better understanding of the regional nature and spatial patterns of PM2.5 pollution, This study proposes a novel framework for delineating regional boundaries of PM2.5 pollution. The framework consists of four steps, including cross-correlation analysis, time-series clustering, generation of Voronoi polygons, and polygon smoothing using polynomial approximation with exponential kernel method. Using the framework, the regional PM2.5 boundaries for China are produced and the boundaries define areas where the monthly PM2.5 time series of any two cities show, on average, more than 50% similarity with each other. These areas demonstrate straightforwardly that PM2.5 pollution is not limited to a single city or a single province. We also found that the PM2.5 areas in China tend to be larger in cold months, but more fragmented in warm months, suggesting that, in cold months, the interactions between PM2.5 concentrations in adjacent cities are stronger than in warmer months. The proposed framework provides a tool to delineate PM2.5 boundaries and identify areas where PM2.5 pollutants interact. It can help define air pollution management zones and assess impacts related to PM2.5 pollution. It can also be used in analyses of other air pollutants.

Changing PM2.5 concentrations in China from 1998 to 2014

Ambient particulate matter air pollution (PM2.5) has been reported as a major pollutant that threatens human health, decreases visibility, and affects the regional and global climate. China, which is among world's most polluted countries of PM2.5, is faced with a severe challenge for PM2.5 mitigation. However, a lack of long-term PM2.5 concentration data on a large spatial scale has hindered comprehensive understanding of spatial and temporal variations of PM2.5 concentrations. Based on the satellite-derived PM2.5 concentration data, we generated maps of PM2.5 concentrations at county level in China. The cartogram and spatial analyst tool in ArcGIS 10.2 are used to visualize the changing temporal and spatial patterns of PM2.5 concentrations.

Modeling Urban PM2.5 Concentration by Combining Regression Models and Spectral Unmixing Analysis in a Region of East China

Understanding the spatial distribution of PM2.5 concentration and its contributing environmental variables is critical to develop strategies of addressing adverse effects of the particulate pollution. In this study, a range of meteorological and land use factors were incorporated into a linear regression (LR) model and a logistic model-based regression (LMR) model to simulate the annual and winter PM2.5 concentrations. The vegetation cover, derived from a linear spectral unmixing analysis (LSUA), and the normalized difference built-up index (NDBI), were found to improve the goodness of fit of the models. The study shows that (1) both the LR and the LMR agree on the predicted spatial patterns of PM2.5 concentration and (2) the goodness of fit is higher for the models established based on the annual PM2.5 concentration than that based on the winter PM2.5. The modeling results show that higher PM2.5 concentration coincided with the major urban area for the annual average but focused on the suburban and rural areas for the winter. The methods introduced in this study can potentially be applied to similar regions in other developing countries.

Atmospheric removal of PM2.5 by man-made Three Northern Regions Shelter Forest in Northern China estimated using satellite retrieved PM2.5 concentration

Atmospheric removal of PM2.5 by the Three Northern Regions Shelter Forest (TNRSF) – the so called Green Great Wall (GGW) in northern China through dry deposition process was estimated using a bulk big-leaf model and a vegetation collection model. Decadal trend of PM2.5 dry deposition flux from 1999 to 2010 was calculated from modeled dry deposition velocity and air concentration retrieved from the satellite remote sensing. Dry deposition velocities of PM2.5 calculated using the two deposition models increased in many places of the TNRSF over the last decade due to increasing vegetation coverage of the TNRSF. Both increasing deposition velocity due to forest expansion and PM2.5 atmospheric level contributed to the increasing deposition flux of PM2.5. The highest atmospheric deposition flux of PM2.5 was found in the Central-north region covering Beijing-Tianjin-Hebei area, followed by the Northwestern and the Northeastern regions of the TNRSF. While greater collection of PM2.5 by vegetation was identified in the Northeastern region of the TNRSF due to higher forest coverage over this region, the most significant incline of the PM2.5 atmospheric removal due to vegetation collection was discerned in the Central-north region because of the most rapid increase in the vegetation coverage in this region. A total mass of 2.85×107t PM2.5 was estimated to be removed from the atmosphere through dry deposition process over the TNRSF from 1999 to 2010. The two deposition models simulated similar magnitude and spatial patterns of PM2.5 dry deposition fluxes. Our results suggest that the TNRSF plays a moderate role in PM2.5 uptake, but enhances PM2.5 atmospheric removal by 30% in 2010 than in 1980.

Long-term trend and spatial pattern of PM2.5 induced premature mortality in China

With rapid economic growth, China has witnessed increasingly frequent and severe haze and smog episodes over the past decade, posing serious health impacts to the Chinese population, especially those in densely populated city clusters. Quantification of the spatial and temporal variation of health impacts attributable to ambient fine particulate matter (PM2.5) has important implications for China's policies on air pollution control. In this study, we evaluated the spatial distribution of premature deaths in China between 2000 and 2010 attributable to ambient PM2.5 in accord with the Global Burden of Disease based on a high resolution population density map of China, satellite retrieved PM2.5 concentrations, and provincial health data. Our results suggest that China's anthropogenic ambient PM2.5 led to 1,255,400 premature deaths in 2010, 42% higher than the level in 2000. Besides increased PM2.5 concentration, rapid urbanization has attracted large population migration into the more developed eastern coastal urban areas, intensifying the overall health impact. In addition, our analysis implies that health burdens were exacerbated in some developing inner provinces with high population density (e.g. Henan, Anhui, Sichuan) because of the relocation of more polluting and resource-intensive industries into these regions. In order to avoid such national level environmental inequities, China's regulations on PM2.5 should not be loosened in inner provinces. Furthermore policies should create incentive mechanisms that can promote transfer of advanced production and emissions control technologies from the coastal regions to the interior regions.