Spatial and Temporal Variations of PM2.5 and Its Relation to Meteorological Factors in the Urban Area of Nanjing, China

Impact of air pollution exposure on the severity of major depressive disorder: Results from the DeprAir study

BACKGROUND

Major depressive disorder (MDD) is one of the most prevalent medical conditions worldwide. Different factors were found to play a role in its etiology, including environmental ones (e.g., air pollution). The aim of this study was to evaluate the association between air pollution exposure and MDD severity.

METHODS

Four hundred sixteen MDD subjects were recruited. Severity of MDD and functioning were evaluated through five rating scales: Montgomery-Asberg Depression Rating Scale (MADRS), Hamilton Depression Rating Scale (HAMD), Clinical Global Impression (CGI), Global Assessment of Functioning (GAF), and Sheehan Disability Scale (SDS). Daily mean estimates of particulate matter with diameter ≤10 (PM10) and 2.5 μm (PM2.5), nitrogen dioxide (NO2), and apparent temperature (AT) were estimated based on subjects' residential addresses. Daily estimates of the 2 weeks preceding recruitment were averaged to obtain cumulative exposure. Multivariate linear and ordinal regression models were applied to assess the associations between air pollutants and MDD severity, overall and stratifying by hypersusceptibility and AT.

RESULTS

Two-thirds of subjects were women and one-third had a family history of depression. Most women had depression with symptoms of anxiety, while men had predominantly melancholic depression. NO2 exposure was associated with worsening of MDD severity (HAMD: β = 1.94, 95% confidence interval [CI], [0.41-3.47]; GAF: β = -1.93, 95% CI [-3.89 to 0.02]), especially when temperatures were low or among hypersusceptible subjects. PM exposure showed an association with MDD severity only in these subgroups.

CONCLUSIONS

Exposure to air pollution worsens MDD severity, with hypersusceptibility and lower temperatures being exacerbating factors.

Research on characteristics and influencing factors of road dust emission in a southern city in China

One of the primary causes of urban atmospheric particulate matter, which is harmful to human health in addition to affecting air quality and atmospheric visibility, is road dust. This study used online monitoring equipment to examine the characteristics of road dust emissions, the effects of temperature, humidity, and wind speed on road dust, as well as the correlation between road and high-space particulate matter concentrations. A section of a real road in Jinhua City, South China, was chosen for the study. The findings demonstrate that the concentration of road dust particles has a very clear bimodal single-valley distribution throughout the day, peaking between 8:00 and 11:00 and 19:00 and 21:00 and troughing between 14:00 and 16:00. Throughout the year, there is a noticeable seasonal change in the concentration of road dust particles, with the highest concentration in the winter and the lowest in the summer. Simultaneously, it has been discovered that temperature and wind speed have the most effects on particle concentration. The concentration of road dust particles reduces with increasing temperature and wind speed. The particle concentrations of road particles and those from urban environmental monitoring stations have a strong correlation, although the trend in the former is not entirely consistent, and the changes in the former occur approximately 1 h after the changes in the latter.

Spatial and Temporal Distribution Characteristics and Cytotoxicity of Atmospheric PM2.5 in the Main Urban Area of Lanzhou City

PM2.5, as one of the most harmful pollutant in the atmospheric environment and population health, has received much attention. We monitored PM2.5 levels at five sampling sites in the Lanzhou City and collected PM2.5 particles from two representative sites for cytotoxicity experiment. The cytotoxicity of PM2.5 samples on A549 cells and migration ability of the cells were respectively detected by Cell Counting kit-8 (CCK-8) assay and scratch assay. We detected the levels of cellular inflammatory factors and oxidative damage-related biochemical indexes. RT-qPCR was used to detect the mRNA levels of NF-κB and epithelial-mesenchymal transition (EMT)-related genes. We found that the Lanlian Hotel station had the highest PM2.5 annual average concentration. The annual average concentration change curve of PM2.5 showed a roughly "U"-shaped distribution during the whole sampling period. The cytotoxicity experiment showed the viability of A549 cells decreased and the scratch healing rate increased in the 200 and 400 μg/mL PM2.5-treated groups. We also found 400 μg/mL PM2.5 induced changes in the mRNA levels of NF-κB and EMT-related genes, the mRNA levels of IKK-α, NIK, and NF-κB in the 400 μg/mL PM2.5 group were higher than those in the control group. The mRNA levels of E-cadherin decreased and α-SMA increased in the 400 μg/mL PM2.5 groups, and the mRNA levels of Fibronectin increased in the 400 μg/mL PM2.5 groups. Moreover, we found hydroxyl radical scavenging ability and T-AOC levels were lower, and LPO levels were higher in the 200 and 400 μg/mL PM2.5 groups, and the SOD activity of cells in the 400 µg/mL PM2.5 group decreased. And compared with the control group, the levels of TNF-α were higher in the 200 and 400 μg/mL PM2.5 groups and the levels of IL-1 were higher in the 400 μg/mL PM2.5 group. The results indicated that the cytotoxicity of atmospheric PM2.5 was related to oxidative damage, inflammatory response, NF-κB activity and EMT.

Quantifying the contribution of environmental variables to cyclists' exposure to PM2.5 using machine learning techniques

Cyclists are particularly vulnerable to travel-related exposure to air pollution. Understanding the factors that increase exposure is crucial for promoting healthier urban environments. Machine learning models have successfully predicted air pollutant concentrations, but they tend to be less interpretable than classical statistical ones, such as linear models. This study aimed to develop a predictive model to assess cyclists' exposure to fine particulate matter (PM2.5) in urban environments. The model was generated using geo-temporally referenced data and machine learning techniques. We explored several models and found that the gradient boosting machine learning model best fitted the PM2.5 predictions, with a minimum root mean square error value of 5.62 μg m-3. The variables with greatest influence on cyclist exposure were the temporal ones (month, day of the week, and time of the day), followed by meteorological variables, such as temperature, relative humidity, wind speed, wind direction, and atmospheric pressure. Additionally, we considered relevant attributes, which are partially linked to spatial characteristics. These attributes encompass street typology, vegetation density, and the flow of vehicles on a particular street, which quantifies the number of vehicles passing a given point per minute. Mean PM2.5 concentration was lower in bicycle paths away from vehicular traffic than in bike lanes along streets. These outcomes underscore the need to thoughtfully design public transportation routes, including bus routes, concerning the network of bicycle pathways. Such strategic planning attempts to improve the air quality in urban landscapes.

Computational and experimental assessment of health risks of fine particulate matter in Nanjing and Yangzhou, China

Fine particulate matter (PM2.5) is a major air pollutant in most cities of China, and poses great health risks to local residents. In this study, the health effects of PM2.5 in Nanjing and Yangzhou were compared using computational and experimental methods. The global exposure mortality model (GEMM), including the results of a cohort study in China, was used to estimate the disease-related risks. Premature mortality attributable to PM2.5 exposure were markedly higher in Nanjing than that in Yangzhou at comparable levels of PM2.5 (8191 95% CI, 6975-9994 vs. 6548 95% CI, 5599-8049 in 2015). However, the baseline mortality rate was on a country-level and the age distribution was on a province-level, traditional estimation method could not accurately represent the health burdens of PM2.5 on a city-level. We proposed a refined calculation method which based on the actual deaths of each city and the disease death rates. Conversely, similar concentrations of PM2.5 exposure resulted in higher actual deaths per million population in Yangzhou (1466 95% CI, 1266-1746) than that in Nanjing (1271 95% CI, 1098-1514). Health risks of PM2.5 are associated with the generation of reactive oxygen species, among which hydroxyl radial (·OH) is the most reactive one. We then collected these PM2.5 samples and quantified the induced ·OH. Consistently, average ·OH concentration in 2015 was higher in Yangzhou than that in Nanjing, again indicating that PM2.5 in Yangzhou was more toxic. The combination of computational and experimental methods demonstrated the complex relationship between health risks and PM2.5 concentrations. The refined estimation method could help us better estimate and interpret the risks caused by PM2.5 exposure on a city-level.

Spatiotemporal heterogeneity of the relationships between PM2.5 concentrations and their drivers in China's coastal ports

PM2.5 is one of the primary air pollutants that affect air quality and threat human health in the port areas. To prevent and control air pollution, it is essential to understand the spatiotemporal distributions of PM2.5 concentrations and their key drivers in ports. 19 coastal ports of China are selected to examine the spatiotemporal distributions of PM2.5 concentrations during 2013-2020. The annual average PM2.5 concentration decreases from 61.03 μg/m3 to 30.17 μg/m3, with an average decrease rate of 51.57%. Significant spatial autocorrelation exists among PM2.5 concentrations of ports. The result of the geographically and temporally weighted regression (GTWR) model shows significant spatiotemporal heterogeneity in the effects of meteorological and socioeconomic factors on PM2.5 concentrations. The effects of boundary layer height on PM2.5 concentrations are found to be negative in most ports, with a stronger effect found in the Pearl River Delta, Yangtze River Delta and some ports of the Bohai Rim Area. The total precipitation shows negative effects on PM2.5 concentrations, with the strongest effect found in ports of the Southeast Coast. The effects of surface pressure on PM2.5 concentrations are positive, with stronger effects found in Beibu Gulf Port and Zhanjiang Port. The effects of wind speed on PM2.5 concentrations generally increase from south to north. Cargo throughput shows strong and positive effects on PM2.5 concentrations in ports of Bohai Rim Area; the positive effects found in Beibu Gulf Port increased from 2013 to 2018 and decreased since 2019. The positive effects of GDP and nighttime light on PM2.5 concentrations gradually decrease and turn negative from south to north. Understandings obtained from this study can potentially support the prevention and control of air pollution in China's coastal ports.

Prediction of PM2.5 concentration based on the CEEMDAN-RLMD-BiLSTM-LEC model

Air quality has emerged as a critical concern in recent years, with the concentration of PM2.5 recognized as a vital index for assessing it. The accuracy of predicting PM2.5 concentrations holds significant value for effective air quality monitoring and management. In response to this, a combined model comprising CEEMDAN-RLMD-BiLSTM-LEC has been introduced, analyzed, and compared against various other models. The combined decomposition method effectively underlines the fundamental characteristics of the data compared to individual decomposition techniques. Additionally, local error correction (LEC) efficiently addresses the issue of prediction errors induced by excessive disturbances. The empirical results of nine steps indicate that the combined CEEMDAN-RLMD-BiLSTM-LEC model outperforms single prediction models such as RLMD and CEEMDAN, reducing MAE, RMSE, and SAMPE by 36.16%, 28.63%, 45.27% and 16.31%, 6.15%, 37.76%, respectively. Moreover, the inclusion of LEC in the model further diminishes MAE, RMSE, and SMAPE by 20.69%, 7.15%, and 44.65%, respectively, exhibiting commendable performance in generalization experiments. These findings demonstrate that the combined CEEMDAN-RLMD-BiLSTM-LEC model offers high predictive accuracy and robustness, effectively handling noisy data predictions and severe local variations. With its wide applicability, this model emerges as a potent tool for addressing various related challenges in the field.

Variability of PM10 level with gaseous pollutants and meteorological parameters during episodic haze event in Malaysia: Domestic or solely transboundary factor?

Haze has become a seasonal phenomenon affecting Southeast Asia, including Malaysia, and has occurred almost every year within the last few decades. Air pollutants, specifically particulate matter, have drawn a lot of attention due to their adverse impact on human health. In this study, the spatial and temporal variability of the PM₁₀ concentration at Kelang, Melaka, Pasir Gudang, and Petaling Jaya during historic haze events were analysed. An hourly dataset consisting of PM₁₀, gaseous pollutants and weather parameters were obtained from Department of Environment Malaysia. The mean PM₁₀ concentrations exceeded the stipulated Recommended Malaysia Ambient Air Quality Guideline for the yearly average of 150 μg/m³ except for Pasir Gudang in 1997 and 2005, and Petaling Jaya in 2013. The PM₁₀ concentrations exhibit greater variability in the southwest monsoon and inter-monsoon periods at the studied year. The air masses are found to be originating from the region of Sumatra during the haze episodes. Strong to moderate correlation of PM₁₀ concentrations was found between CO during the years that recorded episodic haze, meanwhile, the relationship of PM₁₀ level with SO₂ was found to be significant in 2013 with significant negatively correlated relative humidity. Weak correlation of PM₁₀-NOx was measured in all study areas probably due to less contribution of domestic anthropogenic sources towards haze events in Malaysia.

Spatiotemporal Evolution and Driving Forces of PM2.5 in Urban Agglomerations in China

With the rapid development of China's economy, the process of industrialization and urbanization is accelerating, and environmental pollution is becoming more and more serious. The urban agglomerations (UAs) are the fastest growing economy and are also areas with serious air pollution. Based on the monthly mean PM_2.5 concentration data of 20 UAs in China from 2015 to 2019, the spatiotemporal distribution characteristics of PM_2.5 were analyzed in UAs. The effects of natural and social factors on PM_2.5 concentrations in 20 UAs were quantified using the geographic detector. The results showed that (1) most UAs in China showed the most severe pollution in winter and the least in summer. Seasonal differences were most significant in the Central Henan and Central Shanxi UAs. However, the PM_2.5 was highest in March in the central Yunnan UA, and the Harbin-Changchun and mid-southern Liaoning UAs had the highest PM_2.5 in October. (2) The highest PM_2.5 concentrations were located in northern China, with an overall decreasing trend of pollution. Among them, the Beijing-Tianjin-Hebei, central Shanxi, central Henan, and Shandong Peninsula UAs had the highest concentrations of PM_2.5. Although most of the UAs had severe pollution in winter, the central Yunnan, Beibu Gulf, and the West Coast of the Strait UAs had lower PM_2.5 concentrations in winter. These areas are mountainous, have high temperatures, and are subject to land and sea breezes, which makes the pollutants more conducive to diffusion. (3) In most UAs, socioeconomic factors such as social electricity consumption, car ownership, and the use of foreign investment are the main factors affecting PM_2.5 concentration. However, PM_2.5 in Beijing-Tianjin-Hebei and the middle and lower reaches of the Yangtze River are chiefly influenced by natural factors such as temperature and precipitation.

Analyzing the spatio-temporal directions of air pollutants for the initial wave of Covid-19 epidemic over Bangladesh: Application of satellite imageries and Google Earth Engine

One of the most critical issues for city viability and global health is air quality. The shutdown interval for the COVID-19 outbreaks has turned into an ecological experiment, allowing researchers to explore the influence of human/industrial operations on air quality. In this study, we have observed and examined the spatial pattern of air pollutants, specifically CO, NO₂, SO₂, O3 as well as AOD Over Bangladesh. For that reason, the timeline was chosen from March 2019 to October 2020 (before and during the first surge of COVID-19). The full analysis has been performed in Google Earth Engine (GEE). The findings showed that, CO, SO₂, and AOD levels dropped significantly, but SO₂ dropped slowly and O₃ levels were similar, with marginally greater quantities in some areas during the lockdown than in 2019. During the shutdown, the association involving airborne pollutants and weather parameters (temperature and rainfall) revealed that rainfall and temperature were directly associated with air pollutants. COVID-19 mortality had a high positive connection with NO₂ (R² = 0.145; r = 0.38) and AOD (R² = 0.17; r = 0.412). It is also found that various air impurities concentration has a strong relationship with Covid death. It would help the policymakers and officials to gain a better understanding of the sources of atmospheric emissions to develop a substantial proof of short- and long-term mitigation ways to enhance air quality and reduce the associated disease and disability burden.

Spatial Autocorrelation and Temporal Convergence of PM2.5 Concentrations in Chinese Cities

Scientific study of the temporal and spatial distribution characteristics of haze is important for the governance of haze pollution and the formulation of environmental policies. This study used panel data of the concentrations of particulate matter sized < 2.5 μm (PM2.5) in 340 major cities from 1999 to 2016 to calculate the spatial distribution correlation by the spatial analysis method and test the temporal convergence of the urban PM2.5 concentration distribution using an econometric model. It found that the spatial autocorrelation of PM2.5 seemed positive, and this trend increased over time. The yearly concentrations of PM2.5 were converged, and the temporal convergence fluctuated under the influence of specific historical events and economic backgrounds. The spatial agglomeration effect of PM2.5 concentrations in adjacent areas weakened the temporal convergence of PM2.5 concentrations. This paper introduced policy implications for haze prevention and control.

Leveraging Citizen Science and Low-Cost Sensors to Characterize Air Pollution Exposure of Disadvantaged Communities in Southern California

Assessing exposure to fine particulate matter (PM2.5) across disadvantaged communities is understudied, and the air monitoring network is inadequate. We leveraged emerging low-cost sensors (PurpleAir) and engaged community residents to develop a community-based monitoring program across disadvantaged communities (high proportions of low-income and minority populations) in Southern California. We recruited 22 households from 8 communities to measure residential outdoor PM2.5 concentrations from June 2021 to December 2021. We identified the spatial and temporal patterns of PM2.5 measurements as well as the relationship between the total PM2.5 measurements and diesel PM emissions. We found that communities with a higher percentage of Hispanic and African American population and higher rates of unemployment, poverty, and housing burden were exposed to higher PM2.5 concentrations. The average PM2.5 concentrations in winter (25.8 µg/m3) were much higher compared with the summer concentrations (12.4 µg/m3). We also identified valuable hour-of-day and day-of-week patterns among disadvantaged communities. Our results suggest that the built environment can be targeted to reduce the exposure disparity. Integrating low-cost sensors into a citizen-science-based air monitoring program has promising applications to resolve monitoring disparity and capture "hotspots" to inform emission control and urban planning policies, thus improving exposure assessment and promoting environmental justice.

Estimation and Analysis of PM2.5 Concentrations with NPP-VIIRS Nighttime Light Images: A Case Study in the Chang-Zhu-Tan Urban Agglomeration of China

Rapid economic and social development has caused serious atmospheric environmental problems. The temporal and spatial distribution characteristics of PM_2.5 concentrations have become an important research topic for sustainable social development monitoring. Based on NPP-VIIRS nighttime light images, meteorological data, and SRTM DEM data, this article builds a PM_2.5 concentration estimation model for the Chang-Zhu-Tan urban agglomeration. First, the partial least squares method is used to calculate the nighttime light radiance, meteorological elements (temperature, relative humidity, and wind speed), and topographic elements (elevation, slope, and topographic undulation) for correlation analysis. Second, we construct seasonal and annual PM_2.5 concentration estimation models, including multiple linear regression, support random forest, vector regression, Gaussian process regression, etc., with different factor sets. Finally, the accuracy of the PM_2.5 concentration estimation model that results in the Chang-Zhu-Tan urban agglomeration is analyzed, and the spatial distribution of the PM_2.5 concentration is inverted. The results show that the PM_2.5 concentration correlation of meteorological elements is the strongest, and the topographic elements are the weakest. In terms of seasonal estimation, the spring estimation results of multiple linear regression and machine learning estimation models are the worst, the winter estimation results of multiple linear regression estimation models are the best, and the annual estimation results of machine learning estimation models are the best. At the same time, the study found that there is a significant difference in the temporal and spatial distribution of PM_2.5 concentrations. The methods in this article overcome the high cost and spatial resolution limitations of traditional large-scale PM_2.5 concentration monitoring, to a certain extent, and can provide a reference for the study of PM_2.5 concentration estimation and prediction based on satellite remote sensing technology.

Exploring sources and health risks of metals in Beijing PM2.5: Insights from long-term online measurements

To gain a comprehensive understanding of sources, health risks, and regional transport of PM_2.5-bound metals in Beijing, one-year continuous measurement (K, Fe, Ca, Zn, Pb, Mn, Ba, Cu, As, Se, Cr, and Ni) was conducted from December 2016 to November 2017 and Positive Matrix Factorization analysis (PMF) was applied for source apportionment. It was found that the seasonal variation of sources could vary significantly among metals. Sources of Ca, Ba, As, Se, and Cr did not show much seasonal variations, with the contribution of its predominant source higher than 35% in each season. However, the major sources of K, Fe, Zn, Pb, Mn, Cu, and Ni exhibited obvious seasonal variations. In addition, the characteristics of metals in haze episodes were comprehensively investigated. Haze episodes in Beijing were characterized by higher metal concentrations and health risks, which were about 2- 6 times higher than non-haze periods. Moreover, the types of haze episode were different in winter and spring. Haze episodes in winter were mostly influenced by coal combustion, the contribution of which increased greatly and accounted for about 30% of PM_2.5. The metals such as K, Zn, Pb, As, and Se significantly increased, which were mainly transported from south of Beijing. During haze episodes in spring, dust was an important source, which contributed to higher concentrations of crustal metals that transported from northwest of Beijing. To quickly and effectively identify source regions of metals in Beijing during haze episodes, a new diagnostic ratio method using Ca as a reference was developed. The ratios of some anthropogenic metals to Ca significantly increased when air mass was mainly from south of Beijing during haze episodes while the ratios remained constantly low in non-haze periods, when local emissions dominated. This method could be useful for rapid identification and control of metal pollution in Beijing.

Temporal and Spatial Heterogeneity of PM2.5 Related to Meteorological and Socioeconomic Factors across China during 2000-2018

In recent years, air pollution caused by PM_2.5 in China has become increasingly severe. This study applied a Bayesian space-time hierarchy model to reveal the spatiotemporal heterogeneity of the PM_2.5 concentrations in China. In addition, the relationship between meteorological and socioeconomic factors and their interaction with PM_2.5 during 2000-2018 was investigated based on the GeoDetector model. Results suggested that the concentration of PM_2.5 across China first increased and then decreased between 2000 and 2018. Geographically, the North China Plain and the Yangtze River Delta were high PM_2.5 pollution areas, while Northeast and Southwest China are regarded as low-risk areas for PM_2.5 pollution. Meanwhile, in Northern and Southern China, the population density was the most important socioeconomic factor affecting PM_2.5 with q values of 0.62 and 0.66, respectively; the main meteorological factors affecting PM_2.5 were air temperature and vapor pressure, with q values of 0.64 and 0.68, respectively. These results are conducive to our in-depth understanding of the status of PM_2.5 pollution in China and provide an important reference for the future direction of PM_2.5 pollution control.

Assessment of temporal shifting of PM2.5, lockdown effect, and influences of seasonal meteorological factors over the fastest-growing megacity, Dhaka

Dhaka is subjected to high pollution levels throughout the year, holding some relatively high amounts of pollution readings, making its air unhealthy to breathe. The study examined hourly, shifting, seasonal fluctuations in particulate matter (PM_2.5), the effects of seasonal meteorological variables, and the lockdown effect over the megacity of Dhaka from 2019 to 2021 using data from AirNow. The results indicate the daily average PM_2.5 concentration between 2019 and 2021 was 112.49 µg/m³, about four times higher than the WHO limit and two times higher than the Bangladesh standard. Daily PM_2.5 concentrations was high during morning and evening pick-up hours, reaching a maximum hourly concentration of 472.9 µg/m³ in February 2020. The maximum average PM_2.5 concentration was 211.23 µg/m³ in March 2021 (winter season), and the lowest average was 27.58 µg/m³ in August 2020 (rainy season). The Pearson correlation coefficient (r) between the PM_2.5 and meteorological variables were inverse with rainfall (− 0.62), temperature (− 0.73), humidity (− 0.82), but positive with wind (0.09). Daily average Air Quality Index (AQI) concentrations improved from 108.53 to 67.99 µg/m³ during the lockdown period. Finally, the study recommended many mitigation strategies that might assist accountable authorities in lowering the number of life-threatening components in the air.

Spatio-temporal modeling of PM2.5 risk mapping using three machine learning algorithms

Urban air pollution is one of the most critical issues that affect the environment, community health, economy, and management of urban areas. From a public health perspective, PM_2.5 is one of the primary air pollutants, especially in Tehran's metropolis. Owing to the different patterns of PM_2.5 in different seasons, Spatio-temporal modeling and identification of high-risk areas to reduce its effects seems necessary. The purpose of this study was Spatio-temporal modeling and preparation of PM_2.5 risk mapping using three machine learning algorithms (random forest (RF), AdaBoost, and stochastic gradient descent (SGD)) in the metropolis of Tehran, Iran. Therefore, in the first step, to prepare the dependent variable data, the PM_2.5 average was used for the four seasons of spring, summer, autumn, and winter. Then, using remote sensing (RS) and a geographic information system (GIS), independent data such as temperature, maximum temperature, minimum temperature, wind speed, rainfall, humidity, normalized difference vegetation index (NDVI), population density, street density, and distance to industrial centers were prepared as a seasonal average. To Spatio-temporal modeling using machine learning algorithms, 70% of the data were used for training and 30% for validation. The frequency ratio (FR) model was used as input to machine learning algorithms to calculate the spatial relationship between PM_2.5 and the effective parameters. Finally, Spatio-temporal modeling and PM_2.5 risk mapping were performed using three machine learning algorithms. The receiver operating characteristic (ROC) area under the curve (AUC) results showed that the RF algorithm had the greatest modeling accuracy, with values of 0.926, 0.94, 0.949, and 0.949 for spring, summer, autumn, and winter, respectively. According to the RF model, the most important variable in spring and autumn was NDVI. Temperature and distance to industrial centers were the most important variables in the summer and winter, respectively. The results showed that autumn, winter, summer, and spring had the highest risk of PM_2.5, respectively.

Evaluating the influence of land use and land cover change on fine particulate matter

Fine particulate matter (i.e. particles with diameters smaller than 2.5 microns, PM_2.5) has become a critical environmental issue in China. Land use and land cover (LULC) is recognized as one of the most important influence factors, however very fewer investigations have focused on the impact of LULC on PM_2.5. The influences of different LULC types and different land use and land cover change (LULCC) types on PM_2.5 are discussed. A geographically weighted regression model is used for the general analysis, and a spatial analysis method based on the geographic information system is used for a detailed analysis. The results show that LULCC has a stable influence on PM_2.5 concentration. For different LULC types, construction lands have the highest PM_2.5 concentration and woodlands have the lowest. The order of PM_2.5 concentration for the different LULC types is: construction lands > unused lands > water > farmlands >grasslands > woodlands. For different LULCC types, when high-grade land types are converted to low-grade types, the PM_2.5 concentration decreases; otherwise, the PM_2.5 concentration increases. The result of this study can provide a decision basis for regional environmental protection and regional ecological security agencies.

Estimation and Analysis of the Nighttime PM2.5 Concentration Based on LJ1-01 Images: A Case Study in the Pearl River Delta Urban Agglomeration of China

At present, fine particulate matter (PM2.5) has become an important pollutant in regard to air pollution and has seriously harmed the ecological environment and human health. In the face of increasingly serious PM2.5 air pollution problems, feasible large-scale continuous spatial PM2.5 concentration monitoring provides great practical value and potential. Based on radiative transfer theory, a correlation model of the nighttime light radiance and ground PM2.5 concentration is established. A multiple linear regression model is proposed with the light radiance, meteorological elements (temperature, relative humidity, and wind speed) and terrain elements (elevation, slope, and terrain relief) as variables to estimate the ground PM2.5 concentration at 56 air quality monitoring stations in the Pearl River Delta (PRD) urban agglomeration from 2018 to 2019, and the accuracy of model estimation is tested. The results indicate that the R2 value between the model-estimated and measured values is 0.82 in the PRD region, and the model attains a high estimation accuracy. Moreover, the estimation accuracy of the model exhibits notable temporal and spatial heterogeneity. This study, to a certain extent, mitigates the shortcomings of traditional ground PM2.5 concentration monitoring methods with a high cost and low spatial resolution and complements satellite remote sensing technology. This study extends the use of LJ1-01 nighttime light remote sensing images to estimate nighttime PM2.5 concentrations. This yields a certain practical value and potential in nighttime ground PM2.5 concentration inversion.

A Study on Indoor Particulate Matter Variation in Time Based on Count and Sizes and in Relation to Meteorological Conditions

An important aspect of air pollution analysis consists of the varied presence of particulate matter in analyzed air samples. In this respect, the present work aims to present a case study regarding the evolution in time of quantified particulate matter of different sizes. This study is based on data acquisitioned in an indoor location, already used in a former particulate matter-related article; thus, it can be considered as a continuation of that study, with the general aim to demonstrate the necessity to expand the existing network for pollution monitoring. Besides particle matter quantification, a correlation of the obtained results is also presented against meteorological data acquisitioned by the National Air Quality Monitoring Network. The transformation of quantified PM data in mass per volume and a comparison with other results are also addressed.

On-field test and data calibration of a low-cost sensor for fine particles exposure assessment

BACKGROUND

Accurate individual exposure assessment is crucial for evaluating the health effects of particulate matter (PM). Various portable monitors built upon low-cost optical sensors have emerged. However, the main challenge for their application is to guarantee accuracy of measurements.

OBJECTIVE

To assess the performance of a newly developed PM sensor, and to develop methods for post-hoc data calibration to optimize its data quality.

METHOD

We conducted a series of laboratory experiments and field evaluations to quantify the reproducibility within Plantower PM sensors 7003 (PMS 7003) and the consistency between sensors and two established PM_2.5 measurement methods [tapered element oscillating microbalances (TEOM) and gravimetric method (GM)]. Post-hoc data calibration methods for sensors were based on a multiple linear regression model (MLRM) and a random forest model (RFM). Ratios of raw and calibrated readings over the data of reference methods were calculated to examine the improvement after calibration.

RESULTS

Strong correlations (≥0.82) and relatively small relative standard deviations (16-21%) between sensors were found during the laboratory and the field sampling. Compared with the reference methods, moderate to strong coefficients of determination (0.56-0.83) were observed; however, significant deviations were presented. After calibration, the ratios of PMS measurements over that of two reference methods both became convergent.

CONCLUSIONS

Our study validated low-cost optical PM sensors under a wide range of PM_2.5 concentrations (8-167 μg/m³). Our findings indicated potential applicability of PM sensors in PM_2.5 exposure assessment, and confirmed a need of calibration. Linear calibration methods may be sufficient for ambient monitoring using TEOM as a reference, while nonlinear calibration methods may be more appropriate for indoor monitoring using GM as a reference.

Air Pollution Characteristics and Health Risks in the Yangtze River Economic Belt, China during Winter

The air pollution characteristics of six ambient criteria pollutants, including particulate matter (PM) and trace gases, in 29 typical cities across the Yangtze River Economic Belt (YREB) from December 2017 to February 2018 are analyzed. The overall average mass concentrations of PM_2.5, PM₁₀, SO₂, CO, NO₂, and O₃ are 73, 104, 16, 1100, 47, and 62 µg/m³, respectively. PM_2.5, PM₁₀, and NO₂ are the dominant major pollutants to poor air quality, with nearly 83%, 86%, and 59%, exceeding the Chinese Ambient Air Quality Standard Grade I. The situation of PM pollution in the middle and lower reaches is more serious than that in the upper reaches, and the north bank is more severe than the south bank of the Yangtze River. Strong positive spatial correlations for PM concentrations between city pairs within 300 km is frequently observed. NO₂ pollution is primarily concentrated in the Suzhou-Wuxi-Changzhou urban agglomeration and surrounding areas. The health risks are assessed by the comparison of the classification of air pollution levels with three approaches: air quality index (AQI), aggregate AQI (AAQI), and health risk-based AQI (HAQI). When the AQI values escalate, the air pollution classifications based on the AAQI and HAQI values become more serious. The HAQI approach can better report the comprehensive health effects from multipollutant air pollution. The population-weighted HAQI data in the winter exhibit that 50%, 70%, and 80% of the population in the upstream, midstream, and downstream of the YREB are exposed to polluted air (HAQI > 100). The current air pollution status in YREB needs more effective efforts to improve the air quality.

Distribution characteristics and policy-related improvements of PM2.5 and its components in six Chinese cities

This study presents the distribution characteristics and possible sources of fine particulate matter (PM_2.5) and its components, as well as policy-related pollution reduction in the Chinese cities of Jinan, Shijiazhuang (SJZ), Chengdu, Wuxi, Wuhan, and Harbin (HRB). PM_2.5 samples were collected using mid-volume samplers during the autumn of 2017 in all six cities. The samples were analyzed to determine the ambient PM_2.5 compositions, including the concentrations of water-soluble inorganic ions (WSIIs), carbonaceous aerosols, and elements concentrations. The chemical ratios of organic carbon to elemental carbon and nitrate to sulfate as well as the enrichment factors of elements were calculated to establish the possible sources of PM_2.5 in all six cities. The highest PM_2.5 concentration was 152 μg/m³ in SJZ, while the lowest concentration was 47 μg/m³ in HRB. During the sampling period in these six cities, the PM_2.5 concentrations exceeded the World Health Organization recommended daily average air quality guidelines by 2.4-6.1 times, and WSIIs, carbonaceous aerosols, and elements accounted for 31.8%-61.6%, 9.8%-35.1%, and 0.9%-2.5% of the PM_2.5, respectively. In 2013, the Chinese government formulated the Air Pollution Prevention and Control Action Plan (APPCAP) for controlling air pollution, and effective measures have been implemented since then. Compared with previous studies conducted during 2009-2013 before the implementation of the APPCAP, the concentrations of PM_2.5 and most of its components decreased to varying degrees, and large changes in the chemical ratios of PM_2.5 components were observed. These results indicate that PM_2.5 sources vary among these six cities and that China has improved the ambient air quality in these cities through the implementation of air pollution control policies. The APPCAP have achieved considerable results in continuously reducing pollution concentrations, although the air pollution concentrations observed in this study remain high compared with those of other countries.

Effects of socioeconomic and natural factors on air pollution in China: A spatial panel data analysis

China's energy use has increased significantly in recent years with the country's rapid economic growth and large-scale urbanization. Therefore, air pollution has become a major issue. In this study, we conducted spatial autocorrelation and spatial panel regression analyses of sulfur dioxide (SO₂) and nitrogen oxide (NO_X) emissions using the panel data of 31 provincial-level administrative units in China during the period 2011-2017 to comprehensively understand the factors affecting air pollutant emissions. This study contributes to the literature by considering comprehensive factors and spatial effects in the panel-data econometric framework of the whole country of China. The analysis of spatial characteristics shows that during the study period, pollutant emissions in China declined, although emissions in northern regions were still relatively high. Furthermore, SO₂ and NO_X emissions showed significant positive spatial autocorrelations. The results of a fixed-effect spatial lag model showed that both socioeconomic and natural factors were statistically significant for air pollutant emissions, although the degree differed by the type of pollutant. The population, the urbanization rate, the share of added value of secondary industry, and heating and cooling degree days positively affected emissions, while population density, per-capita gross regional product, precipitation, and relative humidity negatively affected emissions. Based on these results, we have put forward suggestions to address the issue of air pollution and achieve environmental sustainability, such as the promotion of regional cooperation and a transition of the economic structure.

Concentrations and mortality due to short- and long-term exposure to PM2.5 in a megacity of Iran (2014-2019)

The present study aimed to survey the spatial and temporal trends of ambient concentration of PM_2.5 and to estimate mortality attributed to short- and long-term exposure to PM_2.5 in Isfahan from March 2014 to March 2019 using the AirQ⁺ software. The hourly concentrations of PM_2.5 were obtained from the Isfahan Department of Environment and Isfahan Air Quality Monitoring Center. Then, the 24-h mean concentration of PM_2.5 for each station was calculated using the Excel software. According to the results, the annual mean concentration of PM_2.5 in 2014-2019 was 29.9-50.9 μg/m³, approximately 3-5 times higher than the WHO guideline (10 μg/m³). The data showed that people of Isfahan in almost 58% to 96% of the days of a year were exposed to PM_2.5 higher than the WHO daily guideline. The concentrations of PM_2.5 in cold months such as October, November, December and January were higher than those in the other months. The zoning of the annual concentrations of PM_2.5 in urban areas showed that the highest PM_2.5 concentrations were related to the northern, northwestern, southern and central areas of the city. On average, from 2014 to 2019, the number of deaths due to natural mortality, lung cancer (LC), chronic obstructive pulmonary disease (COPD), ischemic heart disease (IHD) and stroke associated with ambient PM_2.5 were 948, 16, 18, 281 and 60, respectively. The present study estimated that on average, 14.29% of the total mortality, 17.2% of lung cancer (LC), 15.54% of chronic obstructive pulmonary disease (COPD), 17.12% of ischemic heart disease (IHD) and 14.94% of stroke mortalities were related to long-term exposure to ambient PM_2.5. So provincial managers and politicians must adopt appropriate strategies to control air pollution and reduce the attributable health effects and economic losses.

The changing PM2.5 dynamics of global megacities based on long-term remotely sensed observations

Satellite observations show that the rapid urbanization and emergence of megacities with 10 million or more residents have raised PM2.5 concentrations across the globe during the past few decades. This study examines PM2.5 dynamics for the 33 cities included on the UN list of megacities published in 2018. These megacities were classified into densely (>1500 residents per km²), moderately (300-1500 residents per km²) and sparsely (<300 residents per km²) populated areas to examine the effect of human population density on PM2.5 concentrations in these areas during the period 1998-2016. We found that: (1) the higher population density areas experienced higher PM2.5 concentrations; and (2) the megacities with high PM2.5 concentrations in these areas had higher concentrations than those in the moderately and sparsely populated areas of other megacities as well. The numbers of residents experiencing poor air quality is substantial: approximately 452 and 163 million experienced average annual PM2.5 levels exceeding 10 and 35 μg/m³, respectively in 2016. We also examined PM2.5 trends during the past 18 years and predict that high PM2.5 levels will likely continue in many of these megacities in the future without substantial changes in their economies and/or pollution abatement practices. There will be more megacities in the highest PM2.5 pollution class and the number of megacities in the lowest PM2.5 pollution class will likely not change. Finally, we analyzed how the PM2.5 pollution burden varies geographically and ranked the 33 megacities in terms of PM2.5 pollution in 2016. The most polluted regions are China, India, and South Asia and the least polluted regions are Europe and Japan. None of the 33 megacities currently fall in the WHO's PM2.5 attainment class (<10 μg/m³) while 9 megacities fall into the PM2.5 non-attainment class (>35 μg/m³). In 2016, the least polluted megacity was New York and most polluted megacity was Delhi whose average annual PM2.5 concentration of 110 μg/m³ is nearly three times the WHO's non-attainment threshold.

Re-evaluating the distribution and variation characteristics of haze in China using different distinguishing methods during recent years

Haze is identified via different methods using hourly visibility, relative humidity (RH) and PM_2.5 mass concentration observations collected from 2013 to 2018 at 502 stations in China. An inter-comparison of a new haze identification method (MGB) and other currently used methods (M80 and M90) is performed in this research. Compared with other methods, the MGB method has an advantage in the expression of fine particle pollution characteristics, especially in high humidity areas. The mean value of the correlation coefficient of the daily mean PM_2.5 and daily haze hour obtained by MGB in China is 0.69 which is higher than the correlation coefficients of the daily mean PM_2.5 and haze hour identified by the other two methods. Compared with M80, the haze identified by MGB and M90 is less influenced by daily or monthly variations of RH. Approximately 75% of haze occurs when the RH is exceeds 60% or the PM_2.5 mass concentration is below 105 μg/m³ over China, no matter which haze identification method is used. Haze has obvious regional distribution characteristics and is relatively higher in Beijing-Tianjin-Hebei and its surrounding areas, and the middle and lower reaches of the Yangtze River. The 6-year mean annual total haze identified by the MGB method is 1167 h for mainland China. Compared with MGB, M80 underestimates the haze hour by -34%, and M90 produces a smaller positive overestimation by 18%. The annual total haze hour of China and its three major economic regions shows significant decreasing trends regardless of the identification method used. Daily variation of haze is obtained in this research via automatic visibility measurement. The daily cycles of haze hour identified by MGB and M90 are similar, whereas that identified by M80 behaves differently affected by daily variation of RH. Haze hour is high in winter and low in summer.

The spatiotemporal characteristics of the air pollutants in China from 2015 to 2019

China’s rapid industrialization and urbanization have led to poor air quality, and air pollution has caused great concern among the Chinese public. Most analyses of air pollution trends in China are based on model simulations or satellite data. Studies using field observation data and focusing on the latest data from environmental monitoring stations covering the whole country to assess the latest trends of different pollutants in different regions are relatively rare. The State Council of China promulgated the toughest-ever Air Pollution Prevention and Control Action Plan (Action Plan) in 2013. This led to a major improvement in air quality. We use the hourly Air Quality Index (AQI) and mass concentrations of PM_2.5, PM₁₀, CO, NO₂, O₃, and SO₂ in 362 cities from 2015 to 2019, obtained from the Ministry of Ecology and Environment, to study their temporal and spatial changes and assess the effectiveness of the policy on the atmospheric environment since its promulgation and implementation. We found that the national and regional air quality in China continues to improve, with PM_2.5, PM₁₀, AQI, CO, and SO₂ exhibiting negative trends. However, O₃ and NO₂ pollution is an urgent problem that needs to be solved and the current control strategy for PM_2.5 will only partially reduce the PM_2.5 pollution in the western region. Although the implementation of "Action Plan" measures has effectively improved air quality, China’s air pollution is still serious and far from the WHO standard. Implementing measures for continuous and effective emissions control is still a top priority.

Influence of meteorological conditions on PM2.5 concentrations across China: A review of methodology and mechanism

Air pollution over China has attracted wide interest from public and academic community. PM_2.5 is the primary air pollutant across China. Quantifying interactions between meteorological conditions and PM_2.5 concentrations are essential to understand the variability of PM_2.5 and seek methods to control PM_2.5. Since 2013, the measurement of PM_2.5 has been widely made at 1436 stations across the country and more than 300 papers focusing on PM_2.5-meteorology interactions have been published. This article is a comprehensive review on the meteorological impact on PM_2.5 concentrations_. We start with an introduction of general meteorological conditions and PM_2.5 concentrations across China, and then seasonal and spatial variations of meteorological influences on PM_2.5 concentrations. Next, major methods used to quantify meteorological influences on PM_2.5 concentrations are checked and compared. We find that causality analysis methods are more suitable for extracting the influence of individual meteorological factors whilst statistical models are good at quantifying the overall effect of multiple meteorological factors on PM_2.5 concentrations. Chemical Transport Models (CTMs) have the potential to provide dynamic estimation of PM_2.5 concentrations by considering anthropogenic emissions and the transport and evolution of pollutants. We then comprehensively examine the mechanisms how major meteorological factors may impact the PM_2.5 concentrations, including the dispersion, growth, chemical production, photolysis, and deposition of PM_2.5. The feedback effects of PM_2.5 concentrations on meteorological factors are also carefully examined. Based on this review, suggestions on future research and major meteorological approaches for mitigating PM_2.5 pollution are made finally.

The unidirectional causality influence of factors on PM2.5 in Shenyang city of China

Air quality issue such as particulate matter pollution (PM_2.5 and PM₁₀) has become one of the biggest environmental problem in China. As one of the most important industrial base and economic core regions of China, Northeast China is facing serious air pollution problems in recent years, which has a profound impact on the health of local residents and atmospheric environment in some part of East Asia. Therefore, it is urgent to understand temporal-spatial characteristics of particles and analyze the causality factors. The results demonstrated that variation trend of particles was almost similar, the annual, monthly and daily distribution had their own characteristics. Particles decreased gradually from south to north, from west to east. Correlation analysis showed that wind speed was the most important factor affecting particles, and temperature, air pressure and relative humidity were key factors in some seasons. Path analysis showed that there was complex unidirectional causal relationship between particles and individual or combined effects, and NO₂ and CO were key factors affecting PM_2.5. The hot and cold areas changed little with the seasons. All the above results suggests that planning the industrial layout, adjusting industrial structure, joint prevention and control were necessary measure to reduce particles concentration.

Understanding PM2.5 concentration and removal efficiency variation in urban forest park-Observation at human breathing height

To increase our knowledge of PM_2.5 concentrations near the surface in a forest park in Beijing, an observational study measured the concentration and composition of PM_2.5 in Beijing Olympic Forest Park from 2014 to 2015. This study analyzed the meteorological factors and removal efficiency at 1.5 m above the ground (human breathing height) over the day in the forest. The results showed that the average concentrations of PM_2.5 near the surface peaked at 07:00–09:30 and reached their lowest at 12:00–15:00. Besides, the results showed that the annual concentration of PM_2.5 in the forest was highest during winter, followed by spring and fall, and was lowest during summer. The main chemical components of PM_2.5 near the surface in the forest were SO₄²⁻ and NO₃⁻, which accounted for 68.72% of all water-soluble ions that we observed. The concentration of PM_2.5 in the forest had a significant positive correlation with relative humidity and a significant negative correlation with temperature. The removal efficiency near the surface showed no significant variation through the day or year. In the forest, the highest removal efficiency occurred between 07:00 and 09:30 in summer, while the lowest occurred between 09:30 and 12:00 in winter.

Application of an advanced spatiotemporal model for PM2.5 prediction in Jiangsu Province, China

Either long- or short-term of fine particle (PM_2.5) exposure is associated with adverse health effects especially for children. Primary school students spend much time in schools whereas PM_2.5 prediction for such fine-scale places remains a demanding task, let alone a combined prediction with high temporal resolution. The study aimed to estimate PM_2.5 levels of different time scales for primary schools in Jiangsu Province, China. Hourly PM_2.5 measurements within the academic year (Sept. 2016-June 2017) were collected from 72 routine monitoring sites. Together with PM_2.5 emission inventory and dozens of geographic variables, an advanced spatiotemporal land use regression (LUR) model was employed to estimate PM_2.5 concentrations of biweekly, seasonal and academic year levels in Jiangsu Province at 2457 primary school locations. 10-fold cross-validation verified high prediction ability with squared correlations R_CV² of 0.72 for temporal and 0.71 for spatial changes. PM_2.5 levels in primary schools in Nanjing and Nantong were >10% higher than that of the corresponding cities while pollution levels in primary schools in Xuzhou were >20% lower. For 10 out of the 13 cities in Jiangsu, PM_2.5 levels for primary schools surpassed 70 μg/m³ in winter. Schools in Lianyungang, Zhenjiang and Huai'an suffered the most. This study demonstrated the fine-scale prediction ability of the novel spatiotemporal LUR model, as well as the potential and necessity to apply it in epidemiological studies. It also verified the emergency of pollution control for primary schools from cities such as Lianyungang and Zhenjiang.

Personal exposure to fine particulate matter (PM2.5) of pregnant women during three trimesters in rural Yunnan of China

Little is known about fine particulate matter (PM_2.5) exposure among pregnant women in rural China. This study aims to characterize exposure to PM_2.5 among pregnant women in rural China, and investigate potential risk factors of personal exposure to PM_2.5. The data were obtained from a birth cohort study that enrolled 606 pregnant women in Xuanwei, a county known for its high rates of lung cancer. The personal exposure to PM_2.5 was measured using small portable particulate monitors during each trimester of pregnancy. Participants were interviewed using structured questionnaires that sought information on risk factors of PM_2.5 exposure. The daily exposure to PM_2.5 among the pregnant women ranged from 19.68 to 97.08 μg/m³ (median = 26.08). Exposure to PM_2.5 was higher in winter and autumn than other seasons (p < 0.05); higher during the day than during the night (p < 0.001); and greater during cooking hours than during the rest of the day (p < 0.001). Using a mixed effects model, domestic solid fuel for cooking (β = 1.75, p < 0.001), winter and autumn (β = 2.96, p < 0.001), cooking ≥ once per day (β = 1.58, p < 0.05), heating with coal (β = 1.69, p < 0.001), secondhand smoke exposure (β = 1.59, p < 0.001) and township 1(β = 2.39, p < 0.001) were identified as risk factors for personal exposure to PM_2.5 of pregnant women throughout pregnancy. Indirect effects of season and township factors on personal PM_2.5 exposure were mediated by heating, cooking and domestic fuel using. In conclusion, PM_2.5 levels in Xuanwei exceeded WHO guidelines. Seasonal and township factors and individual behaviors like domestic solid fuel using for cooking, heating with coal and secondhand smoke exposure are associated with higher personal PM_2.5 exposure among pregnant women in rural China.

Spatiotemporal Variations and Factors of Air Quality in Urban Central China during 2013-2015

Spatiotemporal behaviors of particulate matter (PM_2.5 and PM₁₀) and trace gases (SO₂, NO₂, CO, and O₃) in Hefei during the period from December 2013 to November 2015 are investigated. The mean annual PM_2.5 (PM₁₀) concentrations are 89.1 ± 59.4 µg/m³ (118.9 ± 66.8 µg/m³) and 61.6 ± 32.2 µg/m³ (91.3 ± 40.9 µg/m³) during 2014 and 2015, respectively, remarkably exceeding the Chinese Ambient Air Quality Standards (CAAQS) grade II. All trace gases basically meet the requirements though NO₂ and O₃ have a certain upward trend. Old districts have the highest pollution levels, followed by urban periphery sites and new districts. Severe haze pollution occurs in Hefei, with frequent exceedances in particulate matter with 178 (91) days in 2014 (2015). The abnormal PM_2.5 concentrations in June 2014 attributed to agricultural biomass burning from moderate resolution imaging spectroradiometry (MODIS) wildfire maps and aerosol optical depth (AOD) analysis. PM_2.5 is recognized as the major pollutant, and a longer interspecies relationship is found between PM_2.5 and other criteria pollutants for episode days as compared to non-episode days. The air pollution in Hefei tends to be influenced by local primary emissions, secondary formation, and regional transport from adjacent cities and remote regions. Most areas of Anhui, southern Jiangsu, northern Zhejiang, and western Shandong are identified as the common high-potential source regions of PM_2.5. Approximately 9.44 and 8.53 thousand premature mortalities are attributed to PM_2.5 exposure in 2014 and 2015. The mortality benefits will be 32% (24%), 47% (41%), 70% (67%), and 85% (83%) of the total premature mortalities in 2014 (2015) when PM_2.5 concentrations meet the CAAQS grade II, the World Health Organization (WHO) IT-2, IT-3, and Air Quality Guideline, respectively. Hence, joint pollution prevention and control measures need to be strengthened due to pollutant regional diffusion, and much higher health benefits could be achieved as the Hefei government adopts more stringent WHO guidelines for PM_2.5.

Spatiotemporal Variations of Particulate and Gaseous Pollutants and Their Relations to Meteorological Parameters: The Case of Xiangyang, China

High air pollution levels have become a nationwide problem in China, but limited attention has been paid to prefecture-level cities. Furthermore, different time resolutions between air pollutant level data and meteorological parameters used in many previous studies can lead to biased results. Supported by synchronous measurements of air pollutants and meteorological parameters, including PM_2.5, PM₁₀, total suspended particles (TSP), CO, NO₂, O₃, SO₂, temperature, relative humidity, wind speed and direction, at 16 urban sites in Xiangyang, China, from 1 March 2018 to 28 February 2019, this paper: (1) analyzes the overall air quality using an air quality index (AQI); (2) captures spatial dynamics of air pollutants with pollution point source data; (3) characterizes pollution variations at seasonal, day-of-week and diurnal timescales; (4) detects weekend effects and holiday (Chinese New Year and National Day holidays) effects from a statistical point of view; (5) establishes relationships between air pollutants and meteorological parameters. The principal results are as follows: (1) PM_2.5 and PM₁₀ act as primary pollutants all year round and O₃ loses its primary pollutant position after November; (2) automobile manufacture contributes to more particulate pollutants while chemical plants produce more gaseous pollutants. TSP concentration is related to on-going construction and road sprinkler operations help alleviate it; (3) an unclear weekend effect for all air pollutants is confirmed; (4) celebration activities for the Chinese New Year bring distinctly increased concentrations of SO₂ and thereby enhance secondary particulate pollutants; (5) relative humidity and wind speed, respectively, have strong negative correlations with coarse particles and fine particles. Temperature positively correlates with O₃.

Temporal and Spatial Features of the Correlation between PM2.5 and O3 Concentrations in China

In recent years, particulate matter of 2.5 µm or less (PM2.5) pollution in China has decreased but, at the same time, ozone (O3) pollution has become increasingly serious. Due to the different research areas and research periods, the existing analyses of the correlation between PM2.5 and O3 have reached different conclusions. In order to clarify the relationship between PM2.5 and O3, this study selected mainland China as the research area, based on the PM2.5 and O3 concentration data of 1458 air quality monitoring stations, and analyzed the correlation between PM2.5 and O3 for different time scales and geographic divisions. Moreover, by combining the characteristics of the pollutants, topography, and climatic features of the study area, we attempted to discuss the causes of the spatial and temporal differences of R-PO (the correlation between PM2.5 and O3). The study found that: (1) R-PO tends to show a positive correlation in summer and a negative correlation in winter, (2) the correlation coefficient of PM2.5 and O3 is lower in the morning and higher in the afternoon, and (3) R-PO also shows significant spatial differences, including north-south differences and coastland-inland differences.

Using Bayesian spatio-temporal model to determine the socio-economic and meteorological factors influencing ambient PM2.5 levels in 109 Chinese cities

OBJECTIVE

Ambient particulate pollution, especially PM_2.5, has adverse impacts on health and welfare. To manage and control PM_2.5 pollution, it is of great importance to determine the factors that affect PM_2.5 levels. Previous studies commonly focused on a single or several cities. This study aims to analyze the impacts of meteorological and socio-economic factors on daily concentrations of PM_2.5 in 109 Chinese cities from January 1, 2015 to December 31, 2015.

METHODS

To evaluate potential risk factors associated with the spatial and temporal variations in PM_2.5 levels, we developed a Bayesian spatio-temporal model in which the potential temporal autocorrelation and spatial autocorrelation of PM_2.5 levels were taken into account to ensure the independence of the error term of the model and hence the robustness of the estimated parameters.

RESULTS

Daily concentrations of PM_2.5 peaked in winter and troughed in summer. The annual average concentration reached its highest value (79 μg/m³) in the Beijing-Tianjin-Hebei area. The city-level PM_2.5 was positively associated with the proportion of the secondary industry, the total consumption of liquefied petroleum gas and the total emissions of industrial sulfur dioxide (SO₂), but negatively associated with the proportion of the primary industry. A reverse U-shaped relationship between population density and PM_2.5 was found. The city-level and daily-level of weather conditions within a city were both associated with PM_2.5.

CONCLUSION

PM_2.5 levels had significant spatio-temporal variations which were associated with socioeconomic and meteorological factors. Particularly, economic structure was a determinant factor of PM_2.5 pollution rather than per capita GDP. This finding will be helpful for the intervention planning of particulate pollution control when considering the environmental and social-economic factors as part of the strategies.

Acid-extractable heavy metals in PM2.5 over Xi'an, China: seasonal distribution and meteorological influence

To investigate the acid-extractable heavy metals in fine particulate matter (PM_2.5) over Xi'an, China, 24-h PM_2.5 samples were collected every 3 days from December 2015 through November 2016. The bioavailable fraction, termed here the bioavailability index (BI), of PM_2.5-bound metal (As, Ba, Cd, Co, Cu, Mn, Ni, Pb, Ti, V, and Zn) and potential influencing factors, including relative humidity, temperature, air pressure, wind speed, visibility, PM_2.5, and SO₂ concentrations, were assessed in this study. The annual average PM_2.5 concentration was 50.6 ± 35.6 μg m^-3, 1.5 times higher than the Chinese national secondary standard. Zn, Ti, and As were the most abundant elements of those analyzed in the PM_2.5 samples, accounting for 72.1% of total quantity. The seasonal variations and enrichment factor analysis of heavy metals revealed that coal combustion in winter was a crucial source of Pb, Co, Cu, and Zn; and dust resuspension in spring contributed considerable Mn, Ti, and V. The acid-extractable fractions of the measured metals varied. Pb, Cu, Mn, and Zn exhibited relatively high acid-extractable concentrations and BI values. Pb was mostly in the acid-extractable fraction in PM_2.5, with a mean BI value of 66.7%, the highest in summer (69.8%) and lowest in winter (63.7%). Moreover, the BIs of PM_2.5-bound heavy metals were inversely related to temperature and wind speed, whereas positively correlated with relative humidity, SO₂, and PM_2.5 concentration in this study. This study assessed the seasonal distribution and meteorological influence of acid-extractable heavy metals, providing a deeper understanding of atmospheric heavy metal pollution in Xi'an, China.

Analysis and Modelling of PM2.5 Temporal and Spatial Behaviors in European Cities

Particulate matter with an aerodynamic diameter of less than 2.5 µm (PM2.5) is associated with adverse effects on human health (e.g., fatal cardiovascular and respiratory diseases), and environmental concerns (e.g., visibility impairment and damage in ecosystems). This study aimed to evaluate temporal and spatial trends and behaviors of PM2.5 concentrations in different European locations. Statistical threshold models using Artificial Neural Networks (ANN) defined by Genetic Algorithms (GA) were also applied for an urban centre site in Istanbul, to evaluate the influence of meteorological variables and PM10 concentrations on PM2.5 concentrations. Lower PM2.5 concentrations were observed in northern Europe. The highest values were found at traffic-related sites. PM2.5 concentrations were usually higher during the winter and tended to present strong increases during rush hours. PM2.5/PM10 ratios were slightly higher at background sites and the lower values were found in northern Europe (Helsinki and Stockholm). Ratios were usually higher during cold months and during the night. The statistical model (ANN + GA) allowed evaluating the combined effect of different explanatory variables (temperature, wind speed, relative humidity, air pressure and PM10 concentrations) on PM2.5 concentrations, under different regimes defined by relative humidity (threshold value of 79.1%). Important information about the temporal and spatial trends and behaviors related to PM2.5 concentrations in different European locations was developed.

Spatial and temporal variations of air quality and six air pollutants in China during 2015-2017

Air pollution has aroused significant public concern in China, therefore, long-term air-quality data with high temporal and spatial resolution are needed to understand the variations of air pollution in China. However, the yearly variations with high spatial resolution of air quality and six air pollutants are still unknown for China until now. Therefore, in this paper, we analyze the spatial and temporal variations of air quality and six air pollutants in 366 cities across mainland China during 2015–2017 for the first time to the best of our knowledge. The results indicate that the annual mean mass concentrations of PM2.5, PM10, SO₂, and CO all decreased year by year during 2015–2017. However, the annual mean NO₂ concentrations were almost unchanged, while the annual mean O₃ concentrations increased year by year. Anthropogenic factors were mainly responsible for the variations of air quality. Further analysis suggested that PM2.5 and PM10 were the main factors influencing air quality, while NO₂ played an important role in the formation of PM2.5 and O₃. These findings can provide a theoretical basis for the formulation of future air-pollution control policy in China.

Spatiotemporal Characteristics of Air Quality across Weifang from 2014-2018

Air pollution has become a severe threat and challenge in China. Focusing on air quality in a heavily polluted city (Weifang Cty), this study aims to investigate spatial and temporal distribution characteristics of air pollution and identify the influence of weather factors on primary pollutants in Weifang over a long period from 2014-2018. The results indicate the annual Air quality Index (AQI) in Weifang has decreased since 2014 but is still far from the standard for excellent air quality. The primary pollutants are O3 (Ozone), PM10 (Particles with aerodynamic diameter ≤10 µm), and PM2.5 (Particles with aerodynamic diameter ≤10 µm); the annual concentrations of PM10 and PM2.5 show a significant reduction but that of O3 is basically unchanged. Seasonally, PM10 and PM2.5 show a U-shaped pattern, while O3 exhibits inverted U-shaped variations, and different pollutants also present different characteristics daily. Spatially, O3 exhibits a high level in the central region and a low level in the rural areas, while PM10 and PM2.5 are high in the northwest and low in the southeast. Additionally, the concentration of pollutants is greatly affected by meteorological factors, with PM2.5 being negatively correlated with temperature and wind speed, while O3 is positively correlated with the temperature. This research investigated the spatiotemporal characteristics of the air pollution and provided important policy advice based on the findings, which can be used to mitigate air pollution.

Influence of Meteorological Variables and Forest Fires Events on Air Quality in an Urban Area (Córdoba, Argentina)

Extreme environmental events, such as forest fires, are a major emission source of aerosols into the atmosphere. Thus, to investigate the contribution of local forest fires to urban particulate matter, we selected several forest fire indicators, such as number of heat sources, fire events, and burnt area, and collected particles smaller than 2.5 µm (PM_2.5) during a 2.5-year period in Cordoba City (Argentina). Temporal variation of PM_2.5 concentration and composition was described considering fire and nonfire periods, and the influence of meteorological variables was estimated as well. On average, PM_2.5 levels registered in Córdoba city during the study period were lower than values reported for other similar cities in Latin America, despite the fact that during wintertime an increase in PM_2.5 levels was observed due to the occurrence of thermal inversions. Several fire events taking place in the nearby hills around the city during winter and spring 2013 suggest that biomass burning was a strong contribution to urban particles levels, which is consistent with the significant correlation between PM_2.5 concentration and heat sources number. During fire periods, levels of Fe, Ca, and K, were significantly higher than in the nonfire periods, suggesting that these elements can be reliable forest fire markers.

Seasonal Levels, Sources, and Health Risks of Heavy Metals in Atmospheric PM2.5 from Four Functional Areas of Nanjing City, Eastern China

Aerosol pollution is a serious environmental issue, especially in China where there has been rapid urbanization. To identify the intra-annual and regional distributions of health risks and potential sources of heavy metals in atmospheric particles with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5), this work collected monthly PM2.5 samples from urban, industrial, suburban, and rural areas in Nanjing city during 2016 and analyzed the heavy metal compositions (Cu, Pb, Cd, Co, V, Sr, Mn, Ti, and Sb). Enrichment factors (EFs) and principal component analysis (PCA) were applied to investigate the sources. The atmospheric PM2.5 pollution level was highest in the industrial area, followed by the urban and suburban areas, and was the lowest in the rural area. Seasonally, the concentrations of PM2.5 and associated heavy metals in spring and winter were higher than those in summer and autumn. Besides natural sources, heavy metal pollution in PM2.5 might come from metallurgical dust in the industrial area, while it mainly comes from automobile exhaust in urban and suburban areas. Health risk assessments revealed that noncancerous hazards of heavy metals in PM2.5 were low, while the lifetime cancer risks obviously exceeded the threshold. The airborne metal pollution in various functional areas of the city impacted human health differently.

Microclimate simulation and model optimization of the effect of roadway green space on atmospheric particulate matter

Urban green spaces have the potential to mitigate and regulate atmospheric pollution. However, existing studies have primarily focused on the adsorption effect of different plants on atmospheric particulate matter (PM), whereas the effect of green space on PM has not been adequately addressed. In this study, the effect of different urban green space structures and configurations on PM was investigated through the 3D computational fluid dynamics (CFD) model ENVI-met by treating the green space as a whole based on field monitoring, and at the same time, the regulatory effect of green space on PM was examined by integrating information about the forest stand, PM concentration, and meteorological factors. The results show that the green space primarily affected wind speed but had no significant effect on relative humidity, temperature, or wind direction (P > 0.05). The PM concentration was significantly positively correlated with the relative humidity (P < 0.01), significantly negatively correlated with temperature (P < 0.05), but not significantly correlated with wind speed and direction (P > 0.05). Comparison with the measured values reveals that the ENVI-met model well reflected the differences in PM concentrations between different green spaces and the effect of green space on PM. In different green space structures, the uniform-type structure performed rather poorly at purifying PM, the concave-shaped structure performed the best, and the purifying effectiveness of the incremental-type and convex-shaped structure of green space was higher in the rear region than in the front region; in contrast, the degressional-type green space structure was prone to cause aggregation of the PM in the middle region. Broadleaf and broadleaf mixed forests had a better purifying effectiveness on PM than did coniferous forests, mixed coniferous forests, and coniferous broadleaf mixed forests. The above results are of great significance for urban planning and maximizing the use of urban green space resources.

Particulate matter pollution in Chinese cities: Areal-temporal variations and their relationships with meteorological conditions (2015-2017)

As the second largest economy in the world, China experiences severe particulate matter (PM) pollution in many of its cities. Meteorological factors are critical in determining both areal and temporal variations in PM pollution levels; understanding these factors and their interactions is critical for accurate forecasting, comprehensive analysis, and effective reduction of this pollution. This study analyzed areal and temporal variations in concentrations of PM_2.5, PM₁₀, and PM_coarse (PM₁₀ - PM_2.5) and PM_2.5 to PM₁₀ ratios (PM_2.5/PM₁₀) and their relationships with meteorological conditions in 366 Chinese cities from January 1, 2015 to December 31, 2017. On the national scale, PM_2.5 and PM₁₀ decreased from 48 to 42 μg m^-³ and from 88 to 84 μg m^-³, respectively, and the annual mean concentrations were 45 μg m^-³ (PM_2.5) and 84 μg m^-³ (PM₁₀) during the time period (2015-2017). In most regions, largest PM concentrations occurred in winter. However, in northern China, in spring PM_coarse concentrations were highest due to dust. The PM_2.5/PM₁₀ ratio was higher in southern than in northern China. There were large regional disparities in PM diurnal variations. Generally, PM concentrations were negatively correlated with precipitation, relative humidity, air temperature, and wind speed, but were positively correlated with surface pressure. The sunshine duration showed negative and positive impacts on PM in northern and southern cities, respectively. Meteorological factors impacted particulates of different size differently in different regions and over different periods of time.

Risk Reduction Behaviors Regarding PM2.5 Exposure among Outdoor Exercisers in the Nanjing Metropolitan Area, China

Aims: This study aimed to describe risk reduction behaviors regarding ambient particulate matter with a diameter of 2.5 μm or less (PM_2.5) among outdoor exercisers and to explore potential factors influencing those behaviors in the urban area of Nanjing, China. Method: A cross-sectional convenience sample survey was conducted among 302 outdoor exercisers in May 2015. Descriptive analysis was used to describe demographics, outdoor physical activity patterns, knowledge of PM_2.5 and risk reduction behaviors. Multivariate logistic regression analysis was then used to explore factors that influence the adoption of risk reduction behaviors. Results: The most common behavior to reduce PM_2.5 exposure was minimizing the times for opening windows on hazy days (75.5%), and the least common one was using air purifiers (19.3%). Two thirds of respondents indicated that they wore face masks when going outside in the haze (59.5%), but only 13.6% of them would wear professional antismog face masks. Participants adopting risk reduction behaviors regarding PM_2.5 exposure tended to be females, 50–60 year-olds, those with higher levels of knowledge about PM_2.5 and those who had children. Conclusions: These findings indicate the importance of improving knowledge about PM_2.5 among outdoor exercisers. Educational interventions should also be necessary to guide the public to take appropriate precautionary measures when undertaking outdoor exercise in high PM_2.5 pollution areas.