Spatial Characteristics Of PM2 Research Articles

Forecasting of PM2.5 Concentration in Beijing Using Hybrid Deep Learning Framework Based on Attention Mechanism

Air pollution has become a critical factor affecting the health of human beings. Forecasting the trend of air pollutants will be of considerable help to public health, including improving early-warning systems. The article designs a novel hybrid deep learning framework FPHFA (FPHFA is the abbreviation of the title of this paper) for PM2.5 concentration forecasting is proposed, which learns spatially correlated features and long-term dependencies of time series data related to PM2.5. Owing to the complex nonlinear dynamic and spatial features of pollutant data, the FPHFFA model combines multi-channel one-dimensional convolutional neural networks, bi-directional long short-term memory neural networks, and attention mechanisms for the first time. Multi-channel 1D CNNs are applied to capture trend features between some sites and overall spatial characteristics of PM2.5 concentration, Bi LSTMs are used to learn the temporal correlation of PM2.5 concentration, and the attention mechanism is used to focus more effective information at different moments. We carried out experimental evaluations using the Beijing dataset, and the outcomes show that our proposed model can effectively handle PM2.5 concentration prediction with satisfactory accuracy. For the prediction task from 1 to 12 h, our proposed prediction model performs well. The FPHFA also achieves satisfactory results for prediction tasks from 13 to 96 h.

Effect of Vegetation Coverage on the Temporal and Spatial Distribution of PM2.5 Concentration in China's Eight Major Economic Regions from 1998 to 2016

The study researched the relationship between vegetation cover and PM2.5 pollution. The raster NDVI dataset from 1998 to 2016 were reclassified into low, medium, and high vegetation coverage area, and the corresponding PM2.5 concentration in eight economic regions in China were then calculated. On this basis, the temporal and spatial characteristics of PM2.5 pollution were analyzed and Pearson correlation coefficient was used to explore its correlation with NDVI landscape pattern indexes separately from landscape and class level NDVI. The preliminary results showed that:①The northern, eastern, southern coastal, middle reaches of the Yangtze River, and the northeast economic zones have relatively low vegetation coverage in areas with relatively serious PM2.5 pollution. However, the middle reaches of the Yellow River, the southwestern and the Northwestern Economic Zones in areas with relatively low vegetation coverage showed lighter PM2.5 pollution. ②PM2.5 increased in most areas between 1998 and 2016. ③A significant correlation between PM2.5 and NDVI landscape pattern indexes was not found for all areas. ④Therefore, the impacts of the landscape shape index(LSI), percent of landscape(PLAND), number of patches(NP), largest patch index(LPI), and aggregation index(AI) on PM2.5 are heterogeneous.

Difference Equation Model-Based PM 2.5 Prediction considering the Spatiotemporal Propagation: A Case Study of Bohai Rim Region, China

Accurate reporting and prediction of PM 2.5 concentration are very important for improving public health. In this article, we use a spectral clustering algorithm to cluster 44 cities in the Bohai Rim Region. On this basis, we propose a special difference equation model, especially the use of nonlinear diffusion equations to characterize the temporal and spatial dynamic characteristics of PM 2.5 propagation between and within clusters for real-time prediction. For example, through the analysis of PM 2.5 concentration data for 92 consecutive days in the Bohai Rim Region, and according to different accuracy definitions, the average prediction accuracy of the difference equation model in all city clusters is 97% or 90%. The mean absolute error (MAE) of the forecast data for each urban agglomeration is within 7 units μg / m 3 . The experimental results show that the difference equation model can effectively reduce the prediction time, improve the prediction accuracy, and provide decision support for local air pollution early warning and urban comprehensive management.

Inferring Near-Surface PM2.5 Concentrations from the VIIRS Deep Blue Aerosol Product in China: A Spatiotemporally Weighted Random Forest Model

Much of the population is exposed to PM2.5 (particulate matter) pollution in China, and establishing a high-precision PM2.5 grid dataset will be very valuable for air pollution and related studies. However, limited by the traditional models themselves and input data sources, PM2.5 estimations are of low accuracy with narrow spatial coverage. Therefore, we develop a new spatiotemporally weighted random forest (SWRF) model to improve the estimation accuracy and expand the spatial coverage of PM2.5 concentrations using the latest release of the Visible infrared Imaging Radiometer (VIIRS) Deep Blue (DB) aerosol product, along with meteorological variables, and socioeconomic data. Compared with traditional methods and the results of previous similar studies, our satellite-derived PM2.5 distribution shows better consistency with surface-measured records, having a high out-of-sample (out-of-station) cross-validation (CV) coefficient of determination (CV-R2), root mean squared error (RMSE), and mean absolute error (MAE) of 0.87 (0.85), 11.23 (11.53) μg m−3 and 8.25 (8.78) μg m−3, respectively. The monthly, seasonal, and annual mean PM2.5 were also successfully captured (CV-R2 = 0.91–0.92, RMSE = 4.35–6.72 μg m−3). Then, the spatial characteristics of PM2.5 pollution in 2018 were investigated, showing that although air pollution has diminished in recent years, China still faces a high PM2.5 pollution risk overall, especially in winter (average = 50.43 + 16.81 μg m−3). In addition, 19 provinces or administrative regions have annual PM2.5 concentrations >35 μg m−3, particularly the Xinjiang Uygur Autonomous Region (~55.25 μg m−3), Tianjin (~49.65 μg m−3), and Henan Province (~48.60 μg m−3). Our estimated surface PM2.5 concentrations are accurate, which could benefit further research on air pollution in China.

Spatial Characteristics of PM2.5 Pollution among Cities and Policy Implication in the Northern Part of the North China Plain

In the recent decade, the North China Plain (NCP) has been among the region’s most heavily polluted by PM2.5 in China. For the nonattainment cities in the NCP, joint pollution control with related cities is highly needed in addition to the emission controls in their own cities. However, as the basis of decision-making, the spatial characteristics of PM2.5 among these cities are still insufficiently revealed. In this work, the spatial characteristics among all nonattainment cities in the northern part of the North China Plain (NNCP) region were revealed based on data mining technologies including clustering, coefficient of divergence (COD), network correlation model, and terrain and meteorology analysis. The results indicate that PM2.5 pollution of cities with a distance of less than 180 km exhibits homogeneity in the NCP region. Especially, the sub-region, composed of Xinxiang, Hebi, Kaifeng, Zhengzhou, and Jiaozuo, was strongly homogeneous and a strong correlation exists among them. Compared with spring and summer, much stronger correlations of PM2.5 between cities were found in autumn and winter, indicating a strong need for joint prevention and control during these periods. All nonattainment cities in this region were divided into city-clusters, depending on the seasons and pollution levels to further helping to reduce their PM2.5 concentrations effectively. Air stagnation index (ASI) analysis indicates that the strong correlations between cities in autumn were more attributed to the transport impacts than those in winter, even though there were higher PM2.5 concentrations in winter. These results provided an insight into joint prevention and control of pollution in the NCP region.

Temporal and spatial characteristics of PM2.5 transport fluxes of typical inland and coastal cities in China

Local pollution and the cross-boundary transmission of pollutants between cities have an inevitable impact on the atmosphere. Quantitative assessments of the contribution of transport to pollution in inland and coastal cities are necessary for the implementation of practical, regional, and joint emission control strategies. In this study, the Comprehensive Air Quality Model (CAMx), together with the Weather Research and Forecasting model (WRF), was used to simulate the contributions to pollution of different cities in 2016. The monthly inflow, outflow, and net flux from the ground to the extended layers served as the three main indicators for the analysis of the interactions of PM2.5 transport between adjacent cities. Between inland and coastal cities, the magnitude of inflow and outflow are larger in the former than in the latter. The inflow flux in the inland cities (Beijing and Shijiazhuang) was 10.6 and 10.7 kt/day, respectively, while that in the coastal cities (Tianjin, Shanghai, Hefei, Nanjing, and Hangzhou) was 9.1, 3.3, 5.8, 4.4, and 3.7 kt/day, respectively. In terms of variation over the year, the strongest inflow in the BTH region occurred in April, followed by October, July, and January, while that in the coastal cities in YRD occurred in January, followed by October, April, and July. Therefore, based on the flux intensity calculations and the transport flux pathways, effective joint control measures can be provided with scientific support, and a better understanding of the evolutionary mechanism among inland and coastal cities can be acquired.

Asymmetric and Spatial Non-Stationary Effects of Particulate Air Pollution on Urban Housing Prices in Chinese Cities.

Fine particulate matter(PM2.5) pollution will affect people’s well-being and cause economic losses. It is of great value to study the impact of PM2.5 on the real estate market. While previous studies have examined the effects of PM2.5 pollution on urban housing prices, there has been little in-depth research on these effects, which are spatially heterogeneous at different conditional quantiles. To address this issue, this study employs quantile regression (QR) and geographically weighted quantile regression (GWQR) models to obtain a full account of asymmetric and spatial non-stationary effects of PM2.5 pollution on urban housing prices through 286 Chinese prefecture-level cities for 2005–2013. Considerable differences in the data distributions and spatial characteristics of PM2.5 pollution and urban housing prices are found, indicating the presence of asymmetric and spatial non-stationary effects. The quantile regression results show that the negative influences of PM2.5 pollution on urban housing prices are stronger at higher quantiles and become more pronounced with time. Furthermore, the spatial relationship between PM2.5 pollution and urban housing prices is spatial non-stationary at most quantiles for the study period. A negative correlation gradually dominates in most of the study areas. At higher quantiles, PM2.5 pollution is always negatively correlated with urban housing prices in eastern coastal areas and is stable over time. Based on these findings, we call for more targeted approaches to regional real estate development and environmental protection policies.

Using a distributed air sensor network to investigate the spatiotemporal patterns of PM2.5 concentrations

Spatiotemporal variations in PM2.5 are a key factor affecting personal pollution exposure levels in urban areas. However, fixed-site monitoring stations are so sparsely distributed that they hardly capture the dynamic and fine-scale variations in PM2.5 in urban areas with complex geographical features and urban forms. Recently, a distributed air sensor network (DASN) was deployed in Dezhou city, China, to monitor fine-scale air pollution information and obtain deep insight into variations in PM2.5. Based on the data collected by the DASN, this paper investigated the spatiotemporal patterns of PM2.5 using the time-series clustering method. The results demonstrated that there were four stages of PM2.5 daily variations, i.e., accumulation, continuous pollution, dispersion, and cleaning. Generally, the stage of dispersion occurred more rapidly than the stage of accumulation, and PM2.5 accumulated easily in warm and humid weather with low wind speeds. However, the stage of dispersion was affected mainly by high wind speeds and precipitation. Additionally, the results suggested that four variation stages did not strictly correspond to seasonal divisions. The spatial distributions of PM2.5 revealed that the main pollution source was located in a southeastern industrial park, which exhibited a significant impact throughout the four stages. Considering both the temporal and spatial characteristics of PM2.5, this study successfully identified pollution hotspots and confirmed the effect of industrial parks. The study demonstrates that the DASN has high prospective applicability for assessing the fine-scale spatial distribution of PM2.5, and the time-series clustering method can also assist environmental researchers in further exploring the spatiotemporal characteristics of urban air pollution.

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.

Spatial-Temporal Evolution of PM2.5 Concentration and its Socioeconomic Influence Factors in Chinese Cities in 2014–2017

PM2.5 is a main source of China’s frequent air pollution. Using real-time monitoring of PM2.5 data in 338 Chinese cities during 2014–2017, this study employed multi-temporal and multi-spatial scale statistical analysis to reveal the temporal and spatial characteristics of PM2.5 patterns and a spatial econometric model to quantify the socio-economic driving factors of PM2.5 concentration changes. The results are as follows: (1) The annual average value of PM2.5 concentration decreased year by year and the monthly average showed a U-shaped curve from January to December. The daily mean value of PM2.5 concentration had the characteristics of pulse-type fluctuation and the hourly variation presented a bimodal curve. (2) During 2014–2017, the overall PM2.5 pollution reduced significantly, but that of more than two-thirds of cities still exceeded the standard value (35 μg/m3) regulated by Chinese government. PM2.5 pollution patterns showed high values in central and eastern Chinese cities and low values in peripheral areas, with the distinction evident along the same line that delineates China’s uneven population distribution. (3) Population agglomeration, industrial development, foreign investment, transportation, and pollution emissions contributed to the increase of PM2.5 concentration. Urban population density contributed most significantly while economic development and technological progress reduced PM2.5 concentration. The results also suggest that China in general remains a “pollution shelter” for foreign-funded enterprises.

Evolution of the spatiotemporal pattern of PM2.5 concentrations in China – A case study from the Beijing-Tianjin-Hebei region

Atmospheric haze pollution has become a global concern because of its severe effects on human health and the environment. The Beijing-Tianjin-Hebei urban agglomeration is located in northern China, and its haze is the most serious in China. The high concentration of PM2.5 is the main cause of haze pollution, and thus investigating the temporal and spatial characteristics of PM2.5 is important for understanding the mechanisms underlying PM2.5 pollution and for preventing haze. In this study, the PM2.5 concentration status in 13 cities from the Beijing-Tianjin-Hebei region was statistically analyzed from January 2016 to November 2016, and the spatial variation of PM2.5 was explored via spatial autocorrelation analysis. The research yielded three overall results. (1) The distribution of PM2.5 concentrations in this area varied greatly during the study period. The concentrations increased from late autumn to early winter, and the spatial range expanded from southeast to northwest. In contrast, the PM2.5 concentration decreased rapidly from late winter to early spring, and the spatial range narrowed from northwest to southeast. (2) The spatial dependence degree, by season from high to low, was in the order winter, autumn, spring, summer. Winter (from December to February of the subsequent year) and summer (from June to August) were, respectively, the highest and lowest seasons with regard to the spatial homogeneity of PM2.5 concentrations. (3) The PM2.5 concentration in the Beijing-Tianjin-Hebei region has significant spatial spillovers. Overall, cities far from Bohai Bay, such as Shijiazhuang and Hengshui, demonstrated a high-high concentration of PM2.5 pollution, while coastal cities, such as Chengde and Qinhuangdao, showed a low-low concentration.

PM2.5 chemical composition in five European Mediterranean cities: A 1-year study

The seasonal and spatial characteristics of PM2.5 and its chemical composition in the Mediterranean Basin have been studied over a 1-year period (2011–2012) in five European Mediterranean cities: Barcelona (BCN), Marseille (MRS), Genoa (GEN), Venice (VEN), and Thessaloniki (THE). During the year under study, PM10 annual mean concentration ranged from 23 to 46μgm−3, while the respective PM2.5 ranged from 14 to 37μgm−3, with the highest concentrations observed in THE and VEN. Both cities presented an elevated number of exceedances of the PM10 daily limit value, as 32% and 20% of the days exceeded 50μgm−3, respectively. Similarly, exceedances of the WHO guidelines for daily PM2.5 concentrations (25μgm−3) were also more frequent in THE with 78% of the days during the period, followed by VEN with 39%. The lowest PM levels were measured in GEN. PM2.5 exhibited significant seasonal variability, with much higher winter concentrations for VEN and MRS, in fall for THE and in spring for BCN. PM2.5 chemical composition was markedly different even for similar PM2.5 levels. On annual average, PM2.5 was dominated by OM except in THE. OM contribution was higher in Marseille (42%), while mineral matter was the most abundant constituent in THE (32%). Moreover, PM2.5 relative mean composition during pollution episodes (PM2.5>25μgm−3) as well as the origins of the exceedances were also investigated. Results outline mainly the effect of NO3− being the most important driver and highlight the non-negligible impact of atmospheric mixing and aging processes during pollution episodes.

Local and long-range transport influences on PM2.5 at a cities-cluster in northern China, during summer 2008

Hourly PM2.5 concentrations were observed simultaneously at a cities-cluster comprising 10 cities/towns in Hebei province in China from July 1 to 31, 2008. Among the 10 cities/towns, Baoding showed the highest average concentration level (161.57μg/m3) and Yanjiao exhibited the lowest (99.35μg/m3). These observed data were also studied using the joint potential source contribution function with 24-h and 72-h backward trajectories, to identify more clearly the local and countrywide-scale long-range transport sources. For the local sources, three important influential areas were found, whereas five important influential areas were defined for long-range transport sources. Spatial characteristics of PM2.5 were determined by multivariate statistical analyses. Soil dust, coal combustion, and vehicle emissions might be the potential contributors in these areas. The results of a hierarchical cluster analysis for back trajectory endpoints and PM2.5 concentrations datasets show that the spatial characteristics of PM2.5 in the cities-cluster were influenced not only by local sources, but also by long-range transport sources. Different cities in the cities-cluster obtained different weighted contributions from local or long-range transport sources. Cangzhou, Shijiazhuang, and Baoding are near the source areas in the south of Hebei province, whereas Zhuozhou, Yangfang, Yanjiao, Xianghe, and Langfang are close to the sources areas near Beijing and Tianjin.

PM2.5 and PM10-2.5 Compositions during Wintertime Episodes of Elevated PM Concentrations across the Midwestern USA

Northern cities in the Midwest experience frequent PM concentrations in excess of the Federal PM2.5 and PM10 National Ambient Air Quality Standards (NAAQS) during winter, which increasingly outnumber the summertime excesses the further north the cities are located. In contrast, cities located south of the Great Lakes tended to experience a greater number of excesses during summer. In order to ascertain the key components of PM contributing to these winter exceedances, we examined a regional elevated PM concentration episode which occurred between January 31, 2005 and February 6, 2005. The episode was caused by meteorological stagnation which affected 9 states in the Midwestern US (MN, WI, MI, IA, IL, IN, OH, MO, and KY). The temporal and spatial characteristics of PM2.5 and PM10-2.5 mass and bulk chemistry (nitrate, sulfate, ammonium, organic matter) were measured during this and other wintertime elevated PM episodes at Milwaukee, WI, and compared to Chemical Speciation Network (CSN) data. The observed elevated PM episodes demonstrated that nitrate was the driving anthropogenic component of the wintertime exceedances. This led to the conclusion that winter NOx controls on mobile and stationary sources, in combination with sufficient SO2, VOC and NH3 controls, should be further examined for the mitigation of wintertime PM2.5 episodes in the Midwest. The north-south gradient in exceedances of the PM2.5 NAAQS indicated that strategies for avoidance of violations of the 24-hour standard, and to a lesser degree the annual standard, would benefit from an emphasis on different seasons, and therefore different PM components, depending how far north or south the city is located in the Midwest.