Temporal Variations Of PM2 Research Articles

TEMDI: A Temporal Enhanced Multisource Data Integration model for accurate PM2.5 concentration forecasting

Accurate forecasting of PM2.5 concentration is crucial for implementing effective protective measures and mitigating the adverse health impacts of air pollution. To address the complex spatial propagation dynamics and temporal variations of PM2.5, we developed the Temporal Enhanced Multisource Data Integration (TEMDI) model. This innovative approach combines spatial modeling by a Graph Neural Network (GNN) to capture the intricate spatial propagation patterns based on multi-source data fusion, and a novel Time Series Enhancement (TSE) module that includes Ensemble Empirical Mode Decomposition (EEMD), Gated Recurrent Units (GRUs), and a self-attention mechanism to adequately manage the time series’ short-term and long-term trends. Our results demonstrate TEMDI’s superior performance, achieving exceptionally high Probability of Detection (POD) rates of 96.15%, 80.28%, and 71.86% for forecast horizons of 3, 36, and 72 h, respectively. Furthermore, our feature importance analysis reveals that multi-scale features extracted by the EEMD component become increasingly crucial as the prediction horizon extends. The TEMDI model’s ability to provide accurate, reliable PM2.5 forecasts and its enhanced interpretability position it as a valuable tool for guiding environmental policy and management decisions to safeguard public health.

Spatial and temporal variation of PM2.5 and the influence of vegetation in the Yangtze River Delta region

With rapid urbanisation in China, PM2.5 has become a limiting factor for the sustainable development of cities. Taking the Yangtze River Delta as the experimental area, this study analysed the spatial and temporal changes of PM2.5 concentrations from 2001 to 2020. It also examined the variations, dispersion, and correlation with NDVI of PM2.5 concentrations in different vegetation zones at different temporal and spatial scales. The results showed that: (1) The PM2.5 concentration in the Yangtze River Delta showed an overall decreasing trend during 2001–2020, and the change was divided into two phases, starting with an increasing phase and entering a decreasing phase after 2013. (2) In terms of spatial distribution, PM2.5 concentrations in the Yangtze River Delta show a pattern of low in the south and high in the north, with the spatial focus shifting to the north over time. There is a concentration of high levels of particulate matter in the Hefei-Nanjing-Wuxi area. (3) The effect of natural vegetation on the reduction and stabilization of atmospheric particulate matter concentration is better than that of artificial vegetation. (4) Needleleaf forests, broadleaf forests, and shrubs in natural vegetation are more capable of reducing and stabilizing atmospheric particulate matter than grasses. The study can provide a reference for regional air pollution control and regional plant system construction.

Explainable geospatial-artificial intelligence models for the estimation of PM2.5 concentration variation during commuting rush hours in Taiwan

PM2.5 concentrations are higher during rush hours at background stations compared to the average concentration across these stations. Few studies have investigated PM2.5 concentration and its spatial distribution during rush hours using machine learning models. This study employs a geospatial-artificial intelligence (Geo-AI) prediction model to estimate the spatial and temporal variations of PM2.5 concentrations during morning and dusk rush hours in Taiwan. Mean hourly PM2.5 measurements were collected from 2006 to 2020, and aggregated into morning (7 a.m.–9 a.m.) and dusk (4 p.m.–6 p.m.) rush-hour mean concentrations. The Geo-AI prediction model was generated by integrating kriging interpolation, land-use regression, machine learning, and a stacking ensemble approach. A forward stepwise variable selection method based on the SHapley Additive exPlanations (SHAP) index was used to identify the most influential variables. The performance of the Geo-AI models for morning and dusk rush hours had accuracy scores of 0.95 and 0.93, respectively and these results were validated, indicating robust model performance. Spatially, PM2.5 concentrations were higher in southwestern Taiwan for morning rush hours, and suburban areas for dusk rush hours. Key predictors included kriged PM2.5 values, SO2 concentrations, forest density, and the distance to incinerators for both morning and dusk rush hours. These PM2.5 estimates for morning and dusk rush hours can support the development of alternative commuting routes with lower concentrations.

The air we breathe: An In-depth analysis of PM2.5 pollution in 1312 cities from 2000 to 2020.

In recent decades, the phenomenon of rapid urbanization in various parts of the world has led to a significant increase in PM2.5 concentration, which has emerged as a growing social concern. In order to achieve the objective of sustainable development, the United Nations Global Sustainable Development Goals (SDGs) have established the goal of creating inclusive, safe, resilient, and sustainable cities and human habitats (SDG 11). Goal 11.6 aims to decrease the negative environmental impact per capita in cities, with an emphasis on urban air quality and waste management. However, the global distribution of PM2.5 pollution varies due to disparities in urbanization development in different regions. The purpose of this paper is to explore the global spatial distribution and temporal variation of PM2.5 in cities with populations greater than 300,000 from 2000 to 2020, to gain insight into the issue. The findings indicate that PM2.5 concentrations are expected to continue increasing as urbanization progresses, but the rate of evolution of PM2.5 concentration varies depending on the continent, country, and city. From 2000 to 2020, PM2.5 concentration increased significantly in Asia and Africa, with the majority of the increased concentrations located in Asian countries and some African countries. On the other hand, most European and American countries had lower PM2.5 concentrations. The results of this study have the potential to inform urbanization policy formulation by providing knowledge about the spatial distribution of PM2.5 pollution during global urbanization. Addressing the issue of PM2.5 pollution is critical in achieving SDG 11.6 and promoting sustainable and coordinated development in cities worldwide.

Spatiotemporal Patterns and Characteristics of PM2.5 Pollution in the Yellow River Golden Triangle Demonstration Area

Improving air quality in the Yellow River Golden Triangle Demonstration Area (YRGTDA) is an important practice for ecological protection and high-quality development in the Yellow River Basin. Preventing and controlling PM2.5 pollution in this region will require a scientific understanding of the spatiotemporal patterns and characteristics of PM2.5 pollution. PM2.5 data from different sources were combined in this study (the annual average of PM2.5 concentrations were obtained from the Atmospheric Composition Analysis Group of Dalhousie University, and the daily PM2.5 concentration data were obtained from the China National Environmental Monitoring Centre). Then, the temporal variation of PM2.5 concentrations at annual, seasonal, and monthly scales, the spatial variation of PM2.5 concentrations, and the variation of PM2.5 pollution classes were analyzed. Results showed that: (1) at the annual scale, the PM2.5 concentrations showed a decreasing trend from 2000 to 2021 in the study area. The variation of PM2.5 concentrations were divided into two different stages. (2) At the seasonal scale, high PM2.5 concentrations occurred mainly in winter, low PM2.5 concentrations occurred in summer. At the monthly scale, PM2.5 concentrations showed a U-shaped variation pattern from January to December each year. (3) The hotspot analysis of the PM2.5 concentrations in the study area showed a cyclical variation pattern. (4) The PM2.5 concentrations exhibited a spatial pattern of high values in the central and low values in the northern and southern parts of YRGTDA. (5) The number of days for different PM2.5 pollution classes from 2015 to 2021 followed the order of Good > Excellent > Light pollution > Moderate pollution > Heavy pollution > Severe pollution in YRGTDA. The results of this study have great theoretical and practical significance because they reveal the spatiotemporal patterns and pollution characteristics of PM2.5 and will lead to the development of scientifically based measures to reasonably prevent and control pollution in YRGTDA.

The spatial and temporal variation of fine particulate matter pollution in Ethiopia: Data from the Atmospheric Composition Analysis Group (1998-2019).

Evidence suggests ambient fine particulate matter (PM2.5) is a risk factor for cardiovascular diseases, lung cancer morbidity and mortality, and all-cause mortality. Countries that implement strong policies are able to reduce ambient PM2.5 concentration. In Ethiopia, however, PM2.5 monitoring stations, laboratory technicians, and equipment are staggeringly limited. In this study, the spatial and temporal variation of PM2.5 in Ethiopia was assessed. Satellite-based PM2.5 estimates, from the year 1998 to 2019, by Atmospheric Composition Analysis Group (ACAG) at a spatial resolution of 0.01° X 0.01° was used. The annual mean PM2.5 concentration for all administrative regions and zones in Ethiopia was extracted. The average mean from the twenty-two years was also calculated. The trend of PM2.5 concentration was graphed and quantitatively calculated using the Mann-Kendall test. The slope of the change over time was estimated using the Theil-Sen slope. At the zonal administration level, for the average annual mean, spatial dependency using univariate Global Moran's I and clustering and outlier tests using Anselin Local Moran's were performed. The country's average annual mean PM2.5 concentration was 17 μgm-3. The Afar region had the highest concentration, 27.9 μgm-3. The Mann-Kendall S was positive and significant at p<0.001. The spatial distribution of satellite-based ambient PM2.5 concentration was non-random. Significant highest value clustering of ambient total PM2.5 concentration exists in the Afar, Eastern Tigray, and Eastern and Southeastern Amhara while the significant lowest value dispersing was observed in the Southern Oromia and Somali region. At the national and regional levels, the annual mean ambient PM2.5 concentration is beyond the World Health Organization (WHO)-recommended level. The ambient PM2.5 concentration distribution is spatially dependent and significantly clustered in space. Installation of additional ground-based PM2.5 monitoring devices, particularly in regions where PM2.5 concentration is higher, is recommended. Validating satellite-based PM2.5 data with ground-based measurements in the country is also advised.

Cooperative simultaneous inversion of satellite-based real-time PM2.5 and ozone levels using an improved deep learning model with attention mechanism

Ground-level fine particulate matter (PM2.5) and ozone (O3) are air pollutants that can pose severe health risks. Surface PM2.5 and O3 concentrations can be monitored from satellites, but most retrieval methods retrieve PM2.5 or O3 separately and disregard the shared information between the two air pollutants, for example due to common emission sources. Using surface observations across China spanning 2014–2021, we found a strong relationship between PM2.5 and O3 with distinct spatiotemporal characteristics. Thus, in this study, we propose a new deep learning model called the Simultaneous Ozone and PM2.5 inversion deep neural Network (SOPiNet), which allows for daily real-time monitoring and full coverage of PM2.5 and O3 simultaneously at a spatial resolution of 5 km. SOPiNet employs the multi-head attention mechanism to better capture the temporal variations in PM2.5 and O3 based on previous days’ conditions. Applying SOPiNet to MODIS data over China in 2022, using 2019–2021 to construct the network, we found that simultaneous retrievals of PM2.5 and O3 improved the performance compared with retrieving them independently: the temporal R2 increased from 0.66 to 0.72 for PM2.5, and from 0.79 to 0.82 for O3. The results suggest that near-real time satellite-based air quality monitoring can be improved by simultaneous retrieval of different but related pollutants. The codes of SOPiNet and its user guide are freely available online at https://github.com/RegiusQuant/ESIDLM.

PM2.5 source apportionment using organic marker-based chemical mass balance modeling: Influence of inorganic markers and sensitivity to source profiles

A Chemical Mass Balance (CMB) model has been applied to source apportionment of PM2.5 in the Chinese megacity of Chengdu. The study explored the sensitivity of the CMB model to the adoption of different organic source profiles, and to the use of organic markers only (OM-CMB), compared with using a combination of organic and inorganic markers (IOM-CMB). A comprehensive comparison of OM-CMB and IOM-CMB shows that PM2.5 mass concentrations from gasoline vehicles, diesel vehicles, industrial coal combustion, biomass burning, cooking, and SOA which shared same markers in the two methods are in fair to good agreement between the two methods, with the relative biases ranging from 2.2% to 17.3%. The average contributions of sulfate and nitrate sources are more sensitive to the choice of model because inorganic ions were not inputted directly into the OM-CMB. The temporal variations of PM2.5 contributions from sulfate, nitrate, SOA, gasoline vehicles, and biomass burning, characterized by unique markers and low collinearity, were in good agreement between the OM-CMB and IOM-CMB results with the Pearson's r above 0.91 (p < 0.01). However, resuspended dust estimates from OM-CMB had a relatively weak correlation with that from IOM-CMB (Pearson's r = 0.73, p < 0.01), due to the different tracers used. When replacing the source profile for industrial coal combustion with that for residential sources, the contributions of resuspended dust and residential coal combustion were higher, and the contributions of other sources were lower compared with the result for the industrial coal combustion. Different source profiles for gasoline vehicles showed considerable sensitivity of the model to the choice of source profile, even when using data from within a single emissions study. Our results emphasize the value of combining inorganic and organic tracers in minimizing error, and in using up-to-date locally-relevant source profiles in source apportionment of PM.

Spatial and temporal variations in PM2.5 and associated health risk assessment in Saudi Arabia using remote sensing

Air pollutants, especially ambient particulate matter (PM2.5), detrimentally impact human health and cause premature deaths. The dynamic characteristics and associated health risks of PM2.5 are analyzed based on the standard deviational ellipse (SDE) and trend analysis in Saudi Arabia (SAU) from 1998 to 2018 by utilizing recently updated satellite-derived PM2.5 concentrations (V4.GL.03). The outcomes show that the national average PM2.5 concentration increased from 28 μg/m3 to 45 μg/m3 with a growth rate of 2.3 μg/m3/year. The center of median PM2.5 concentrations moved to the southeast over the years studied due to the presence of vast sandy deserts, sand dunes, a busy port, and coastal and industrial areas in this region. The areas of SAU that experienced PM2.5 concentrations above 35 μg/m3 increased from 20% to 70%. The rapid-fast growth (RFG) class acquired from the unsupervised classification has the fastest growth rate of 2.5 μg/m3/yr, occurring in southeastern SAU, namely Ash-Sharqiyah, Ar-Riyad, and Najran. It covered ∼27% of the total area of SAU over the study period. Whereas, the slow growth (SG) class with a less than 0.2 μg/m3/yr growth rate covered 12% of the total area of SAU, distributed in northwestern regions. The extent of extremely-high risk areas corresponding to greater than 1 × 103 μg·person/m3 increased from 4% to 11%, particularly in Makkah, Central Al-Madinah, and western Asir, Jizan, mid-eastern Najran, Al-Quassim, and mid-eastern Ar-Riyad and Ash Sharqiyah.

Temporal variability of PM2.5 and its possible sources at the tropical megacity, Bengaluru, India.

The mass concentrations of PM2.5 were measured at a tropical megacity, Bengaluru, India, for the year 2015. The mean mass concentrations showed large fluctuations on day to day basis with values less than the Indian National Ambient Air Quality Standard (INAAQS) of 60µgm-3. The observed annual mean mass concentration of 28 ± 11µgm-3 is also within the INAAQS value of 40µgm-3. The diurnal trend of PM2.5 concentration showed bimodal distribution, with the primary peak in the morning and the secondary one during the late evening hours. The timing of the peaks matched with rush traffic hours. Strong seasonality is observed in the diurnal concentration of PM2.5 with the highest value during winter (50 ± 22µgm-3) and the lowest of (11 ± 5µgm-3) in the monsoon. The weekend PM2.5 mass concentrations were less than those on the weekdays up to a maximum of 100%. The decrease in PM2.5 mass concentration was also observed on the day of the strike when many busses were off the road. Vehicular traffic is suggested as one of the primary contributors of PM2.5 in this region. The health risk assessment in this study, points to ischemic heart diseaseas the primary cause of PM2.5-induced death.

Determination of Satellite-Derived PM2.5 for Kampala District, Uganda

Ground monitoring stations are widely used to monitor particulate matter (PM2.5). However, they are expensive to maintain and provide information localized to the stations, and hence are limited for large-scale use. Analysis of in situ PM2.5 shows that it varies spatially and temporally with distinct seasonal differences. This study, therefore, explored the use of satellite images (Sentinel-2 and Landsat-8) for determining the spatial and temporal variations in PM2.5 for Kampala District in Uganda. Firstly, satellite-derived aerosol optical depth (AOD) was computed using the Code for High Resolution Satellite mapping of optical Thickness and aNgstrom Exponent algorithm (CHRISTINE code). The derived AOD was then characterised with reference to meteorological factors and then correlated with in situ PM2.5 to determine satellite-derived PM2.5 using geographically weighted regression. In the results, correlating in situ PM2.5 and AOD revealed that the relationship is highly variable over time and thus needs to be modelled for each satellite’s overpass time, rather than having a generic model fitting, say, a season. The satellite-derived PM2.5 showed good model performance with coefficient of correlation (R2) values from 0.69 to 0.89. Furthermore, Sentinel-2 data produced better predictions, signifying that increasing the spatial resolution can improve satellite-derived PM2.5 estimations.

Meteorological Influences on Spatiotemporal Variation of PM2.5 Concentrations in Atmospheric Pollution Transmission Channel Cities of the Beijing-Tianjin-Hebei Region, China.

Understanding the spatiotemporal characteristics of PM2.5 concentrations and identifying their associated meteorological factors can provide useful insight for implementing air pollution interventions. In this study, we used daily air quality monitoring data for 28 air pollution transmission channel cities in the Beijing–Tianjin–Hebei region during 2014–2019 to quantify the relative contributions of meteorological factors on spatiotemporal variation in PM2.5 concentration by combining time series and spatial perspectives. The results show that annual mean PM2.5 concentration significantly decreased in 24 of the channel cities from 2014 to 2019, but they all still exceeded the Grade II Chinese Ambient Air Quality Standards (35 μg m−3) in 2019. PM2.5 concentrations exhibited clear spatial agglomeration in the most polluted season, and their spatial pattern changed slightly over time. Meteorological variables accounted for 31.96% of the temporal variation in PM2.5 concentration among the 28 cities during the study period, with minimum temperature and average relative humidity as the most critical factors. Spatially, atmospheric pressure and maximum temperature played a key role in the distribution of PM2.5 concentration in spring and summer, whereas the effect of sunshine hours increased greatly in autumn and winter. These findings highlight the importance of future clean air policy making, but also provide a theoretical support for precise forecasting and prevention of PM2.5 pollution.

Spatial and temporal variations of PM2.5 concentrations in Chinese cities during 2015-2019

ABSTRACT The study analyzed the current status and changing trends of PM2.5 pollution, and used Kriging spatial interpolation, spatial autocorrelation analysis, and scan statistics to explore the spatiotemporal characteristics and identify hotspots. The results showed that PM2.5 pollution during 2015–2019 displayed a downward trend year by year, with a pronounced seasonal difference of higher concentrations in winter and lower concentrations in summer. By 2019, there were still 110 cities (n = 194) failed to meet China’s annual grade II air quality standard (35 μg/m3). The spatial distribution of PM2.5 was characterized by marked heterogeneity, with a significant spatial dependence and clustering characteristics. The pollution hotspots of PM2.5 were mainly concentrated in eastern and central China, especially in the Beijing-Tianjin-Hebei region and its surrounding area. The results of this study will assist Chinese authorities in developing strategies for preventing and controlling air pollution, especially in hotspot regions and during peak periods.

Association of Long-term Exposure to PM2.5 with Blood Lipids in the Chinese Population: Findings from a Longitudinal Quasi-experiment

BACKGROUND AND AIM: Although epidemiological studies on the effect of chronic fine particulate matter (PM2.5) exposure on lipid disorders have been conducted, it is unclear if improved air quality is associated with beneficial changes in the blood lipid profile. In China, clean air actions introduced in 2013 have rapidly reduced the concentration of ambient PM2.5. We aim to explore the relationship between temporal variations in PM2.5 and changes in blood lipids. METHODS: We conducted a change-by-change study, based on two waves (2011 and 2015) of a national survey of the same 5,111 Chinese adults before and after the clean air actions, respectively. Long-term PM2.5 exposure was assessed using a state-of-the-art estimator at the city level. Based on the within-individual differences between the two waves, we associated PM2.5 changes with the variations of four lipid biomarkers—triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C)—using a mixed-effects regression model. The robustness and homogeneity of the association were tested via sensitivity analyses. RESULTS:For each 10 μg/m3 reduction in PM2.5, LDL-C, and TC decreased by 2.71 (95% confidence intervals [CI] 0.10–5.32) and 4.16 (95% CI 1.24–7.08) mg/dL, respectively. There was no significant association with HDL-C or TG. The results were robust among models adjusted for different covariates. PM2.5 was a significant risk factor for dyslipidemia with an adjusted relative risk of 1.21 (95% CI 1.09, 1.34). The association between PM2.5 and LDL-C was stronger with the older or the adults who did not take medications. CONCLUSIONS:The results suggest that PM2.5 exert a cardiotoxic effect by increasing the risk of lipid disorders. Improvement of air quality could prevent dyslipidemia by reducing LDL-C and TC levels. Clean air policies should be implemented as public health measures in countries with aging societies, especially developing ones with a high air pollution burden. KEYWORDS: Cardiovascular diseases, Environmental epidemiology, Particulate matter, Long-term exposure, Policy and practice

Characteristics and Sources of PM2.5 Pollution in Typical Cities of the Central Plains Urban Agglomeration in Autumn and Winter

To study the characteristics of PM2.5 pollution and the potential sources of its main components in the central plain urban agglomeration in autumn and winter, PM2.5 samples were collected continually in the four typical cities of Zhengzhou, Luoyang, Anyang, and Xinxiang from October 2018 to January 2019. X-ray fluorescence spectrometry, carbon analysis methods, and ion chromatography were used to determine 18 kinds of inorganic elements, organic carbon (OC)/elemental carbon (EC), and 9 kinds of water-soluble inorganic ions. According to the daily PM2.5 concentration, three pollution levels were divided, and the comparative analysis for the spatial and temporal variation of PM2.5 and its main components, i.e., NO3-, OC, and 18 kinds of inorganic elements, were studied via the calculation of the nitrogen oxidation rate (NOR), secondary organic carbon (SOC), and enrichment factor. The emission sources and their contribution rates of PM2.5 pollution level in the four cities were calculated by a chemical mass balance (CMB) model; the potential pollution sources of PM2.5 and its main components, NO3- and OC, in the four cities were analyzed by a backward trajectory model (HYSPLIT) and potential source contribution factor method (PSCF). The results showed that the means of PM2.5 in Zhengzhou, Luoyang, Anyang, and Xinxiang were (82.1±45.5), (84.7±39.8), (96.8±46.1), and (81.1±36.6) μg·m-3, respectively, during the sampling period, and the maximum daily mean values were 3.3, 2.6, 3.0, and 2.3 times, respectively, of the Chinese national secondary standard; the main components of PM2.5 in the four cities were NO3- and SOC, and the concentration of NO3-, the ratio of NO3-/EC, and NOR all increased significantly with the rising of pollution levels, generally showing that the mean values of NO3-/EC and NOR of Zhengzhou and Luoyang were a little higher than those of Anyang and Xinxiang; the concentration of SOC, the proportion of SOC in OC, and the ratio of SOC/EC all increased with the rising of pollution levels. From the concentration and enrichment degree of inorganic elements, As was the highest in Zhengzhou; Mn and Fe were the highest in Luoyang; Zn, Ni, and Cr were the highest in Anyang; and Cu and Pb were the highest in Xinxiang. Secondary nitrate, secondary sulfate, organic matter, coal combustion, motor vehicle, dust, biomass burning, and industrial processes were the main PM2.5 pollution sources in the four cities, with the highest contribution rate of secondary nitrate in Zhengzhou (37.7%), the highest contribution rate of vehicle sources in Xinxiang (14.1%), and a relatively high contribution rate of industrial process source in Luoyang (7.0%) and Anyang (6.8%). The northwest direction of airflow contributed 51.6%, 49.2%, 49.6%, and 46.3% of the total airflow in Zhengzhou, Luoyang, Anyang, and Xinxiang, respectively. From the potential pollution area of each city, the Zhengzhou area was mainly concentrated in Henan province, the Luoyang area was mainly concentrated in the south of Henan province and Fen-wei plain, and the Anyang and Xinxiang areas were mainly concentrated in Henan province and the Beijing-Tianjin-Hebei transport belt. The pollution levels of OC in Anyang and Xinxiang were also affected by the northwest Anhui, southwest Shandong, southeast Shanxi, and north Shaanxi.

Evaluation of Variability in the Ambient PM2.5 Concentrations from FEM and FRM-like Measurements for Exposure Estimates

This study aims to evaluate the variability in ambient fine particulate matter (PM2.5) concentrations obtained from the federal equivalent method (FEM) and federal reference method (FRM)-like measurements at the national air quality monitoring stations (AQMSs) for exposure estimates and to examine the effect of environmental factors and sampling site characteristics affecting the spatial and temporal variations in PM2.5 concentrations. A mixed-effects model was used to evaluate the temporal and spatial variability in daily and annual PM2.5 concentrations during 2014-2017 at 16 AQMSs in the Big Taipei City, Taiwan. The mean FEM PM2.5 concentrations were ~30% higher than the FRM-like PM2.5 concentrations. The FRM-like PM2.5 concentrations obtained by applying the calibration procedures presented a negligible between-site variability. The daily FEM PM2.5 concentrations were dominated by the within-site variability (~90%), whereas the annual concentrations were reasonably attributable to the between-site variability (47.8%). Ambient PM2.5 was mainly affected by the gaseous pollutants (such as NO2, O3, and SO2), accounting for 45.8% and 26.8% of the within-site and between-site variability in concentrations, respectively. The FEM measurements rather than the FRM-like measurements at the AQMSs could provide a higher between-site variability for exposure estimates of PM2.5 in the epidemiological studies.

Comparison of Modeled-to-Monitored PM2.5 Exposure Concentrations Resulting from Transportation Emissions in a Near-Road Community

Residents living in near-road communities are exposed to traffic-related air pollutants, which can adversely affect their health. Near-road communities are expected to observe significant spatial and temporal variations in pollutant concentrations. Determining these variations in the surrounding areas can help raise awareness among government agencies of these underserved communities living near highways. This study conducted traffic and air quality measurements along with emission and dispersion modeling of the exposure to transportation emissions of a near-road urban community adjacent to the US 54 highway (US 54), with annual average daily traffic (AADT) of 107,237. The objectives of this study were (i) to develop spatial and temporal patterns of pollutant concentration variation and (ii) to apportion the differences in exposure concentrations to background concentrations and those that are contributed from major highways. It was observed that: (a) particulate matter (PM2.5) in near-road communities is dominated by the regional background concentrations which account for more than 85% of the pollution; and (b) only near-road receptors are affected by the traffic-related air pollutant emissions from major highways while spatial and temporal variations of PM2.5 concentrations in near-road communities are less influenced by local traffic, subsiding rapidly to negligible concentrations at 300 m from the road. Modeled PM2.5 concentrations were compared with monitored data. For better air quality impact assessments, higher quality data such as time-specific traffic volume and fleet information as well as site-specific meteorological data could help yield more accurate concentration predictions. Modeled-to-monitored comparison shows that air quality in near-road communities is dominated by regional background concentrations.

Similarities and Differences in the Temporal Variability of PM2.5 and AOD Between Urban and Rural Stations in Beijing

Surface particulate matter with an aerodynamic diameter of &lt;2.5 μm (PM2.5) and column-integrated aerosol optical depth (AOD) exhibits substantial diurnal, daily, and yearly variabilities that are regionally dependent. The diversity of these temporal variabilities in urban and rural areas may imply the inherent mechanisms. A novel time-series analysis tool developed by Facebook, Prophet, is used to investigate the holiday, seasonal, and inter-annual patterns of PM2.5 and AOD at a rural station (RU) and an urban station (UR) in Beijing. PM2.5 shows a coherent decreasing tendency at both stations during 2014–2018, consistent with the implementation of the air pollution action plan at the end of 2013. RU is characterized by similar seasonal variations of AOD and PM2.5, with the lowest values in winter and the highest in summer, which is opposite that at UR with maximum AOD, but minimum PM2.5 in summer and minimum AOD, but maximum PM2.5 in winter. During the National Day holiday (1–7 October), both AOD and PM2.5 holiday components regularly shift from negative to positive departures, and the turning point generally occurs on October 4. AODs at both stations steadily increase throughout the daytime, which is most striking in winter. A morning rush hour peak of PM2.5 (7:00–9:00 local standard time (LST)) and a second peak at night (23:00 LST) are observed at UR. PM2.5 at RU often reaches minima (maxima) at around 12:00 LST (19:00 LST), about four hours later (earlier) than UR. The ratio of PM2.5 to AOD (η) shows a decreasing tendency at both stations in the last four years, indicating a profound impact of the air quality control program. η at RU always begins to increase about 1–2 h earlier than that at UR during the daytime. Large spatial and temporal variations of η suggest that caution should be observed in the estimation of PM2.5 from AOD.

Spatial and Temporal Variability of Pm2.5 Air Polution Level in Kharkiv City

Purpose of the study is to determine spatial and temporal variability of PM2.5 air pollution level within urban geosystems with a high anthropogenic pressure (on the example of Kharkiv city). Methods. Open online monitoring platform «Air Pollution», PM2.5 level was measured with «7bit Pollution Monitor», statistical. Results. Monitoring of PM2.5 air pollution of Kharkiv was conducted from August 1, 2019 to April 1, 2020 at 6 control points in different districts of the city. Air Pollution open online monitoring platform served as a data source of 28,119 mass concentrations of PM2.5 in the air. Threshold concentrations, recommended by the WHO, were used to determine compliance with hygienic standards for fine dust of less than 2.5 μm. The PM2.5 air pollution level and its degree of threat to public health were assessed in accordance with the International Air Quality Index (AQI) scale. Fine particles pose the greatest health risk. Exceeding the PM2.5 threshold level set by the WHO was detected for 17.4% of studied cases (4,905 out of 28,119 studied cases). the AQI scale, in the vast majority of cases, the content of PM2.5 in the air varies from low to moderate. The highest level of PM2.5 in the air was measured at point No. 1 at the address Aptekarskyi Lane 9, which may be due to its location in areas with high traffic. The daily, weekly and seasonal variability of PM2.5 content was determined. Seasonal and daily variations in the content PM2.5 of in the air of Kharkiv depend on changes in weather conditions (precipitation, wind speed, etc.), the level of traffic congestion and the operation mode of potential stationary pollution sources. Conclusions. The obtained results provide a basis for further study of the correlation of PM2.5 content in the air with the influence of various natural and socio-economic factors of the territory, as well as with the mortality rates. In general, according to

Source apportionment of airborne particulate matters over the Athabasca oil sands region: Inter-comparison between PMF modeling and ground-based remote sensing

The airborne particulate matters (PM) emissions associated with oil sands mining and processing operations in Athabasca oil sands regions (AOSR) could impact ambient air quality. But to what scales the effects could geographically reach remains unclear. The study was conducted to investigate the geographical influence of PM emissions within the AOSR at three sampling sites. The first site is sandwiched by oil sands mining and processing facilities. The second site is an urban site and close to the oil sands mining and processing areas. The third site is a remote rural site. Over the 2-year period (2015 and 2016), the observed geometric mean PM2.5 concentrations at Fort McKay (in AOSR), Fort McMurray (near AOSR), and Anzac (far away from AOSR) were 4.81, 5.89, and 3.30 μg/m3, respectively. The temporal variations of PM2.5 showed more elevated concentrations in spring and summer than winter and fall, which was consistent with the aerosol optical depth (AOD) observation. The Positive Matrix Factorization (PMF) modeling results at the above three sites suggest that anthropogenic sources were the dominant contributors of ambient aerosol concentrations within AOSR. According to the depleted vanadium (V) content in the surface dust factor from near AOSR site to remote site, the influence of petroleum coke dust as the primary source on aerosol emissions is geographically limited. The result also revealed the considerably long lasting influence of bitumen spill on the local aerosol source contributions. From the ground-based remote sensing observations of aerosol optical properties, petroleum coke could influence the atmospheric aerosol levels over AOSR with highly light-absorbing coarse-mode aerosols under warm and dry weather conditions.