Spatiotemporal distribution characteristics of PM2.5 concentration in China from 2000 to 2018 and its impact on population

Prediction of developmental toxic effects of fine particulate matter (PM2.5) water-soluble components via machine learning through observation of PM2.5 from diverse urban areas

The global health implications of fine particulate matter (PM2.5) underscore the imperative need for research into its toxicity and chemical composition. In this study, zebrafish embryos exposed to the water-soluble components of PM2.5 from two cities (Harbin and Hangzhou) with differences in air quality, underwent microscopic examination to identify primary target organs. The Harbin PM2.5 induced dose-dependent organ malformation in zebrafish, indicating a higher level of toxicity than that of the Hangzhou sample. Harbin PM2.5 led to severe deformities such as pericardial edema and a high mortality rate, while the Hangzhou sample exhibited hepatotoxicity, causing delayed yolk sac absorption. The experimental determination of PM2.5 constituents was followed by the application of four algorithms for predictive toxicological assessment. The random forest algorithm correctly predicted each of the effect classes and showed the best performance, suggesting that zebrafish malformation rates were strongly correlated with water-soluble components of PM2.5. Feature selection identified the water-soluble ions F- and Cl- and metallic elements Al, K, Mn, and Be as potential key components affecting zebrafish development. This study provides new insights into the developmental toxicity of PM2.5 and offers a new approach for predicting and exploring the health effects of PM2.5.

Time series analysis of PM2.5 pollution risk based on the supply and demand of PM2.5 removal service: a case study of the urban areas of Beijing

Demonstrating the temporal changes in PM2.5 pollution risk in regions facing serious PM2.5 pollution problems can provide scientific evidence for the air pollution control of the region. However, research on the variation of PM2.5 pollution risk on a fine temporal scale is very limited. Therefore, we developed a method for quantitative characterizing PM2.5 pollution risk based on the supply and demand of PM2.5 removal services, analyzed the time series characteristics of PM2.5 pollution risk, and explored the reasons for the temporal changes using the urban areas of Beijing as the case study area. The results show that the PM2.5 pollution risk in the urban areas of Beijing was close between 2008 and 2012, decreased by approximately 16.3% in 2016 compared to 2012, and further decreased by approximately 13.2% in 2021 compared to 2016. The temporal variation pattern of the PM2.5 pollution risk in 2016 and 2021 showed significant differences, including an increase in the number of risk-free days, a decrease in the number of heavily polluted days, and an increase in the stability of the risk day sequence. The significant reduction in risk level was mainly attributed to Beijing's air pollution control measures, supplemented by the impact of COVID-19 control measures in 2021. The results of PM2.5 pollution risk decomposition indicate that compared to the previous 2 years, the stability and predictability of the risk variation in 2016 increased, but the overall characteristics of high risk from November to February and low risk from April to September did not change. The high risk from November to February was mainly due to the demand for coal heating during this period, a decrease in PM2.5 removal service supply caused by plant leaf fall, and the common occurrence of temperature inversions in winter, which hinders the diffusion of air pollutants. This study provides a method for the analysis of PM2.5 pollution risk on fine temporal scales and may provide a reference for the PM2.5 pollution control in the urban areas of Beijing.

Multi-timescale variation characteristics of PM2.5 in different regions of China during 2014-2022

Over the past decade, China has achieved a significant reduction in PM2.5 concentrations. Due to the diversity of natural and artificial factors, regional differences are remarkable in the variation characteristics and have not been well addressed in previous studies. Based on hourly observed PM2.5 concentrations from 2014 to 2022, this study conducted a comprehensive analysis of variation characteristics on annual, seasonal, and diurnal scales, with a special focus on differences across major regions. Driving factors of the variations, the effectiveness of air pollution control efforts as well as future priorities were discussed. The annual PM2.5 concentrations in all regions showed an overall downward trend from 2014 to 2022, but the decline rates differed notably across the regions, with the maximum value nearly two times higher than the minimum value. The seasonal decline rates also differ from region to region, which could be partially attributed to the burning of crop residues and dust events. Northeast China was significantly affected by the burning of crop residues and experienced a big drop in the number of fire points in autumn, but a remarkable increase in spring. The spring dust events may greatly contribute to PM2.5 concentrations in northern and western China. For diurnal variation, nighttime concentrations were generally greater than daytime concentrations, and the nighttime concentrations were likely to increase in eastern regions and decrease in western regions. Furthermore, the daytime and nighttime ratios (calculated by daytime/nighttime concentration divided by the daily-mean concentration) exhibited different interannual trends, with the daytime ratios decreasing and nighttime ratios increasing, especially in the northeastern and western regions. The findings indicate that the air pollution control efforts have been generally successful, but with large regional disparities, and highlight the importance of controlling crop residue burning, dust events, and nighttime emissions for specific seasons and regions.

Spatiotemporal empirical analysis of particulate matter PM2.5 pollution and air quality index (AQI) trends in Africa using MERRA-2 reanalysis datasets (1980-2021)

In this study, the spatial-temporal trends of PM2.5 pollution were analyzed for subregions in Africa and the entire continent from 1980 to 2021. The distributions and trends of PM2.5 were derived from the monthly concentrations of the aerosol species from MERRA-2 reanalysis datasets comprising of sulphates (SO4), organic carbon (OC), black carbon (BC), Dust2.5 and Sea Salt (SS2.5). The resulting PM2.5 trends were compared with the climate factors, socio-economic indicators, and terrain characteristics. Using the Mann-Kendall (M-K) test, the continent and its subregions showed positive trends in PM2.5 concentrations, except for western and central Africa which exhibited marginal negative trends. The M-K trends also determined Dust2.5 as the dominant contributing aerosol factor responsible for the high PM2.5 concentrations in the northern, western and central regions of Africa, while SO4 and OC were respectively the most significant contributors to PM2.5 in the eastern and southern Africa regions. For the climate factors, the PM2.5 trends were determined to be positively correlated with the wind speed trends, while precipitation and temperature trends exhibited low and sometimes negative correlations with PM2.5. Socio-economically, highly populated, and bare/sparse vegetated areas showed higher PM2.5 concentrations, while vegetated areas tended to have lower PM2.5 concentrations. Topographically, low laying regions were observed to retain the deposited PM2.5 especially in the northern and western regions of Africa. The Air Quality Index (AQI) results showed that 94 % of the continent had an average PM2.5 of 12-35 μg/m3 hence classified as "Moderate" AQI, and the rest of the continent's PM2.5 levels was between 35 and 55 μg/m3 implying AQI classification of "Unhealthy for Sensitive People". Northern and western Africa regions had the highest AQI, while southern Africa had the lowest AQI. The approach and findings in this study can be used to complement the evaluation and management of air quality in Africa.

Long-term variation, solubility and transport pathway of PM2.5-bound iron in a megacity of northern China

Although particulate Fe has a significant impact on human health, atmospheric chemical reactions, air quality, climate change, and ecosystems, there is a lack of long-term continuous hourly observation on particulate Fe in the megacity of Beijing, limiting research on these issues. To address this gap, this study continuously measured hourly concentrations of Fe in PM2.5 from October 2018 to October 2022 in Beijing. The results indicate an overall decline in Fe concentrations, consistent with previous studies in Beijing. This decline can be attributed to multiple factors, such as reduced coal consumption, restrictions on biomass burning, increased use of clean energy, advanced technologies for industrial emission reduction, and efforts to control fugitive dust. Seasonal variations in Fe concentrations were similar across the various years, with higher mean concentrations in spring, fall, and winter, and lower levels in summer. Daily variations in PM2.5-bound Fe concentrations exhibited two peaks, influenced by changes in emission intensity and the evolution of the planetary boundary layer. The solubility of PM2.5-bound Fe exhibited a wide range, varying from 4 % to 95 %, surpassing previously reported source-specific values. This variability can be attributed to acid dissolution effects and complexation behaviors. Nonparametric wind regression analysis identified distinct hotspots (higher concentrations) in the northwest wind sector at wind speeds of approximately 5-15 km/h, which are associated with blowing dust and dust storms. Additionally, the potential source contribution function analysis identified high-potential source areas were precisely located in the northwestern, western, and southern regions of Beijing, rather than primarily in the southern areas recorded in a previous study. This research provides valuable insights for studying the health effects and migration and transformation of nutrient elements, particularly particulate Fe, in Beijing.

Long- and short-term health benefits attributable to PM2.5 constituents reductions from 2013 to 2021: A spatiotemporal analysis in China

Long- and short-term exposure to constituents of fine particulate matter (PM2.5) substantially affects human health. However, assessments of the health and economic benefits of reducing PM2.5 constituents are scarce. This study estimates the number of premature deaths from all-cause, cardiovascular (CVD), and respiratory diseases avoided due to reductions in daily and annual average concentrations of PM2.5 constituents. The Environmental Benefits Mapping and Analysis Program was used for two scenarios: we used yearly concentrations of PM2.5 constituents from 2013 to 2020 as the baseline concentration surface (Scenario I), and 2021 as the baseline year (Scenario II). With reductions in daily and annual average concentrations of PM2.5 constituents, 309,099 (95 % confidence interval [CI]: 37,265-571,485) and 195,297 (95 % CI: 178,192-211,914) premature deaths were avoided in Scenario I, respectively; meanwhile, 347,296 (95 % CI: 79,258-604,758) and 201,567 (95 % CI: 185,038-217,530) premature deaths were avoided in Scenario II, respectively. Moreover, economic benefits associated with the prevention of premature deaths were estimated using the willingness to pay (WTP) and modified human capital (AHC) methods. The total estimated economic benefits amounted to 563.32 billion RMB (WTP) and 322.03 billion RMB (AHC) in Scenario I. In Scenario II, the associated economic benefits were 751.48 billion RMB (WTP) and 427.56 billion RMB (AHC), accounting for 0.657 and 0.374 % of China's gross domestic product in 2021, respectively. Additionally, we analyzed the sensitivity of CVD-related premature deaths to the concentrations of PM2.5 constituents, and found that CVD-related premature deaths were more sensitive to black carbon.

Assessment of variability in PM2.5 and its impact on human health in a West African country

PM2.5 has become a global challenge threatening human health, climate, and the environment. PM2.5 is ranked as the most common cause of premature mortality and morbidity. Therefore, the current study endeavors to probe the spatiodynamic characteristics of PM2.5 in the Republic of Niger and its impacts on human health from 1998 to 2019. Based on remotely sensed satellite datasets, the study found that the concentration of PM2.5 continued to rise in Niger from 68.85 μg/m3 in 1998 to 70.47 μg/m3 in 2019. During the study period, the annual average PM2.5 concentration is far above the WHO guidelines and the interim target-1 (35 μg/m3). The overall annual growth rate of PM2.5 concentration in Niger is 0.02 μg/m3/year. The health risk (HR) due to PM2.5 exposure is also escalated in Niger, particularly, in Southern Niger. The extent of the extremely high-risk areas corresponding to 1 × 104-9.4 × 105 μg.persons/m3 is increased from 0.9% (2000) to 2.8% (2019). Niamey, southern Dakoro, Mayahi, Tessaoua, Mirriah, Magaria, Matameye, Aguié, Madarounfa, Groumdji, Madaoua, Bouza, Keita, eastern Tahoua, eastern Illéla, Bkomnni, southern Dogon-Doutchi, Gaya, eastern Boboye, central Kollo, and western Tillabéry are experienced high HR due to long-term exposure to PM2.5. These findings indicate that PM2.5 causes a serious health risk across Niger. There is an immediate need to carry out its regional control. Therefore, policymakers and the Nigerien government should make conscious efforts to identify the priority target areas with radically innovative appropriate mitigation interventions.

Transcriptomics profile of human bronchial epithelial cells exposed to ambient fine particles and influenza virus (H3N2)

Fine particulate matter (PM2.5) pollution remains a major threat to public health. As the physical barrier against inhaled air pollutants, airway epithelium is a primary target for PM2.5 and influenza viruses, two major environmental insults. Recent studies have shown that PM2.5 and influenza viruses may interact to aggravate airway inflammation, an essential event in the pathogenesis of diverse pulmonary diseases. Airway epithelium plays a critical role in lung health and disorders. Thus far, the mechanisms for the interactive effect of PM2.5 and the influenza virus on gene transcription of airway epithelial cells have not been fully uncovered. In this present pilot study, the transcriptome sequencing approach was introduced to identify responsive genes following individual and co-exposure to PM2.5 and influenza A (H3N2) viruses in a human bronchial epithelial cell line (BEAS-2B). Enrichment analysis revealed the function of differentially expressed genes (DEGs). Specifically, the DEGs enriched in the xenobiotic metabolism by the cytochrome P450 pathway were linked to PM2.5 exposure. In contrast, the DEGs enriched in environmental information processing and human diseases, such as viral protein interaction with cytokines and cytokine receptors and epithelial cell signaling in bacterial infection, were significantly related to H3N2 exposure. Meanwhile, co-exposure to PM2.5 and H3N2 affected G protein-coupled receptors on the cell surface. Thus, the results from this study provides insights into PM2.5- and influenza virus-induced airway inflammation and potential mechanisms.

Temporal and spatial distributions, source identification, and health risk assessment of polycyclic aromatic hydrocarbons in PM2.5 from 2016 to 2021 in Shenzhen, China

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous contaminants in the atmosphere that have drawn intense attention due to their carcinogenicity and mutagenicity. In this work, 1424 air samples were collected between January 2016 and December 2021 in three areas of Shenzhen, China to determine the concentrations of PM2.5 and PAHs and their spatiotemporal variation. Human health risks due to the daily intake and uptake of PAHs and the resulting incremental lifetime cancer risk (ILCR) were also evaluated. PAHs were detected frequently in the samples at concentrations between 0.28 and 32.7 ng/m3 (median: 1.04 ng/m3). PM2.5 and PAH concentrations decreased from 2016 to 2021, and the Yantian area had lower median concentrations of PM2.5 (23.0 μg/m3) and PAHs (0.02 ng/m3) than the Longgang and Nanshan areas. The concentrations of PM2.5 and PAHs were significantly higher in winter than in summer. Analysis of diagnostic ratios indicated that petroleum combustion was the dominant source of airborne PAHs in Shenzhen. The estimated daily intake (EDI) and uptake (EDU) of PAHs by local residents decreased gradually with increasing age, indicating that infants are at particular risk of PAH exposure. However, the incremental lifetime cancer risks (ILCRs) were below the threshold value of 10-6, indicating that inhalation exposure to PAHs posed a negligible carcinogenic risk to Shenzhen residents. While promising, these results may underestimate actual PAH exposure levels, so further analysis of health risks due to PAHs in Shenzhen is needed.

Exposure to Fine Particulate Matter Constituents and Human Semen Quality Decline: A Multicenter Study

Exposure to fine particulate matter (PM < 2.5 μm in diameter [PM2.5]) may accelerate human sperm quality decline, although research on this association is limited. Our objective was to investigate the relationship between exposure to the chemical constituents of PM2.5 air pollution and decreased sperm quality and to further explore the exposure-response relationship. We conducted a multicenter population-based cohort study including 78,952 semen samples from 33,234 donors at 6 provincial human sperm banks (covering central, northern, southern, eastern, and southwestern parts of China) between 2014 and 2020. Daily exposure to PM2.5 chemical composition was estimated using a deep learning model integrating a density ground-based measure network at a 1 km resolution. Linear mixed models with subject- and center-specific intercepts were used to quantify the harmful impacts of PM2.5 constituents on semen quality and explore their exposure-response relationships. Per interquartile range (IQR) increase in PM2.5 exposure levels during spermatogenesis was significantly associated with decreased sperm concentration, progressive motility, and total motility. For PM2.5 constituents, per IQR increment in Cl- (β: -0.02, 95% CI: [-0.03, -0.00]) and NO3- (β: -0.05, 95% CI: [-0.08, -0.02]) exposure was negatively associated with sperm count, while NH4+ (β: -0.03, 95% CI: [-0.06, -0.00]) was significantly linked to decreased progressive motility. These results suggest that exposure to PM2.5 chemical constituents may adversely affect human sperm quality, highlighting the urgent need to reduce PM2.5 exposure.

Long term spatiotemporal trends and health risk assessment of remotely sensed PM2.5 concentrations in Nigeria

PM_2.5 is an important indicator reflecting air quality variations. Currently, environmental pollution related issues have become more severe that significantly threaten human health. The current study is an attempt to analyze the spatio-dynamic characteristics of PM_2.5 in Nigeria based on the directional distribution and trend clustering analysis from 2001 to 2019. The results indicated that PM_2.5 concentration increased in most of the Nigerian states, particularly in mid-northern and southern states. The lowest PM_2.5 concentration in Nigeria is even beyond the interim target-1 (35 μg/m³) of the WHO. During the study period, the average PM_2.5 concentration increased at a growth rate of 0.2 μg/m³/yr from 69 μg/m³ to 81 μg/m³. The growth rate varied from region to region. Kano, Jigawa, Katsina, Bauchi, Yobe, and Zamfara experienced the fastest growth rate of 0.9 μg/m³/yr with 77.9 μg/m³ mean concentration. The median center of the national average PM_2.5 moved toward the north indicating the highest PM_2.5 concentration in northern states. The Saharan desert dust is the dominant source of PM_2.5 in northern areas. Moreover, agricultural practices and deforestation activities along with low rainfall increase desertification and air pollution in these regions. Health risks increased in most of the mid-northern and southern states. The extent of ultra-high health risk (UHR) areas corresponding to the 8×10⁴-7.3×10⁶ μg⋅person/m³ increased from 1.5% to 2.8%. Mainly Kano, Lagos, Oyo, Edo, Osun, Ekiti, southeastern Kwara, Kogi, Enugu, Anambra, Northeastern Imo, Abia, River, Delta, northeastern Bayelsa, Akwa Ibom, Ebonyi, Abuja, Northern Kaduna, Katsina, Jigawa, central Sokoto, northeastern Zamfara, central Borno, central Adamawa, and northwestern Plateau are under UHR areas.

Exploring the effect of ecological land structure on PM2.5: A panel data study based on 277 prefecture-level cities in China

In the context of serious urban air pollution and limited land resources, it is important to understand the environmental value of ecological land. Previous studies focused mostly on the effectiveness of a particular type of green space or the total amount of ecological land on PM_2.5 and have rarely analyzed the association between ecological land structure and PM_2.5 systematically and quantitatively. Therefore, we took 277 cities in China as an example, comprehensively compared the results of different models, and selected a spatial Durbin model using time-fixed effects to dissect the degree of influence of ecological land and different land types within it on PM_2.5. The urban ecological land use structure was closely related to PM_2.5, and the higher the proportion of ecological land use was, the lower the PM_2.5. The degree and direction of influence of different types of land functions within ecological land on PM_2.5 differed, with forests, shrubs, and grasslands causing a weakening impact on PM_2.5, while wetlands and waters did not have a weakening role. The degree of reduction of PM_2.5 by a single type of ecological land was significantly smaller than that by a composite type of ecological land. Green space should be comprehensively considered, designed and adjusted in urban planning to continuously optimize the ecological spatial structure, increase landscape diversity and maximize ecological benefits. The findings of this study help with exploring the effects of land use structure under the goal-oriented control of air pollution and provide theoretical reference and decision-making support for formulating precise air pollution control policies and optimizing the spatial development of national land.

County-Based PM2.5 Concentrations’ Prediction and Its Relationship with Urban Landscape Pattern

Satellite top-of-atmosphere (TOA) reflectance has been validated as an effective index for estimating PM2.5 concentrations due to its high spatial coverage and relatively high spatial resolution (i.e., 1 km). For this paper, we developed an emsembled random forest (RF) model incorporating satellite top-of-atmosphere (TOA) reflectance with four categories of supplemental parameters to derive the PM2.5 concentrations in the region of the Yangtze River Delta-Fujian (i.e., YRD-FJ) located in east China. The landscape pattern indices at two levels (i.e., type level and overall level) retrieved from 3-year land classification imageries (i.e., 2016, 2018, and 2020) were used to discuss the correlation between county-based PM2.5 values and landscape pattern. We achieved a cross validation R2 of 0.91 (RMSE = 9.06 μg/m3), 0.89 (RMSE = 10.19 μg/m3), and 0.90 (RMSE = 8.02 μg/m3) between the estimated and observed PM2.5 concentrations in 2016, 2018, and 2020, respectively. The PM2.5 distribution retrieved from the RF model showed a trend of a year-on-year decrease with the pattern of “Jiangsu > Shanghai > Zhejiang > Fujian” in the YRD-FJ region. Our results also revealed that the landscape pattern of farmland, water bodies, and construction land exhibited a highly positive relationship with the county-based average PM2.5 values, as the r coefficients reached 0.74 while the forest land was negatively correlated with the county-based PM2.5 (r = 0.84). There was also a significant correlation between the county-based PM2.5 and shrubs (r = 0.53), grass land (r = 0.76), and bare land (r = 0.60) in the YRD-FJ region, respectively. Three landscape pattern indices at an overall level were positively correlated with county-based PM2.5 concentrations (r = 0.80), indicating that the large landscape fragmentation, edge density, and landscape diversity would raise the PM2.5 pollution in the study region.

Drivers and Decoupling Effects of PM2.5 Emissions in China: An Application of the Generalized Divisia Index

Although economic growth brings abundant material wealth, it is also associated with serious PM_2.5 pollution. Decoupling PM_2.5 emissions from economic development is important for China's long-term sustainable development. In this paper, the generalized Divisia index method (GDIM) is extended by introducing innovation indicators to investigate the main drivers of PM_2.5 pollution in China and its four subregions from 2008 to 2017. Afterwards, a GDIM-based decoupling index is developed to examine the decoupling states between PM_2.5 emissions and economic growth and to identify the main factors leading to decoupling. The obtained results show that: (1) Innovation input scale and GDP are the main drivers for increases in PM_2.5 emissions, while innovation input PM_2.5 intensity, emission intensity, and emission coefficient are the main reasons for reductions in PM_2.5 pollution. (2) China and its four subregions show general upward trends in the decoupling index, and their decoupling states turn from weak decoupling to strong decoupling. (3) Innovation input PM_2.5 intensity, emission intensity, and emission coefficient contribute largely to the decoupling of PM_2.5 emissions. Overall, this paper provides valuable information for mitigating haze pollution.