PM10 Values Research Articles

Aerosols in the Mixed Layer and Mid-Troposphere from Long-Term Data of the Italian Automated Lidar-Ceilometer Network (ALICENET) and Comparison with the ERA5 and CAMS Models

Aerosol vertical stratification significantly influences the Earth’s radiative balance and particulate-matter-related air quality. Continuous vertically resolved observations remain scarce compared to surface-level and column-integrated measurements. This work presents and makes available a novel, long-term (2016–2022) aerosol dataset derived from continuous (24/7) vertical profile observations from three selected stations (Aosta, Rome, Messina) of the Italian Automated Lidar-Ceilometer (ALC) Network (ALICENET). Using original retrieval methodologies, we derive over 600,000 quality-assured profiles of aerosol properties at the 15 min temporal and 15 metre vertical resolutions. These properties include the particulate matter mass concentration (PM), aerosol extinction and optical depth (AOD), i.e., air quality legislated quantities or essential climate variables. Through original ALICENET algorithms, we also derive long-term aerosol vertical layering data, including the mixed aerosol layer (MAL) and elevated aerosol layers (EALs) heights. Based on this new dataset, we obtain an unprecedented, fine spatiotemporal characterisation of the aerosol vertical distributions in Italy across different geographical settings (Alpine, urban, and coastal) and temporal scales (from sub-hourly to seasonal). Our analysis reveals distinct aerosol daily and annual cycles within the mixed layer and above, reflecting the interplay between site-specific environmental conditions and atmospheric circulations in the Mediterranean region. In the lower troposphere, mixing processes efficiently dilute particles in the major urban area of Rome, while mesoscale circulations act either as removal mechanisms (reducing the PM by up to 35% in Rome) or transport pathways (increasing the loads by up to 50% in Aosta). The MAL exhibits pronounced diurnal variability, reaching maximum (summer) heights of > 2 km in Rome, while remaining below 1.4 km and 1 km in the Alpine and coastal sites, respectively. The vertical build-up of the AOD shows marked latitudinal and seasonal variability, with 80% (30%) of the total AOD residing in the first 500 m in Aosta-winter (Messina-summer). The seasonal frequency of the EALs reached 40% of the time (Messina-summer), mainly in the 1.5–4.0 km altitude range. An average (wet) PM > 40 μg m−3 is associated with the EALs over Rome and Messina. Notably, 10–40% of the EAL-affected days were also associated with increased PM within the MAL, suggesting the entrainment of the EALs in the mixing layer and thus their impact on the surface air quality. We also integrated ALC observations with relevant, state-of-the-art model reanalysis datasets (ERA5 and CAMS) to support our understanding of the aerosol patterns, related sources, and transport dynamics. This further allowed measurement vs. model intercomparisons and relevant examination of discrepancies. A good agreement (within 10–35%) was found between the ALICENET MAL and the ERA5 boundary layer height. The CAMS PM10 values at the surface level well matched relevant in situ observations, while a statistically significant negative bias of 5–15 μg m−3 in the first 2–3 km altitude was found with respect to the ALC PM profiles across all the sites and seasons.

Spatio-Temporal Distribution of PM2.5 and PM10 Concentrations and Assessment of Public Health Risk in the Three Most Polluted Provinces of Iran

This study examines the spatio-temporal variations of ambient air pollution from fine particulates below 2.5 µm (PM2.5) and particulate matter below 10 µm (PM10) in three of the most polluted provinces in Iran, namely Tehran, Isfahan, and Khuzestan, over a 6-year period (2016–2021). The results reveal distinct patterns of PM10 and PM2.5 concentrations since in Tehran, the highest PM10 and PM2.5 levels occur in winter, while PM2.5 is lowest from March to May. Khuzestan experiences the highest pollution levels in summer due to dust storms, while Isfahan exhibits pollution levels and annual patterns similar to Tehran. Strong correlations are observed between PM10 and PM2.5 concentrations at stations in Tehran and Khuzestan Provinces, suggesting common sources and variation in both coarse and fine PM, with average PM2.5/PM10 ratios of 0.39–0.42, suggesting the dominance of dust. Furthermore, the analysis identifies the role of atmospheric stability, wind speed, and dust storms in controlling the PM levels in the three provinces. Lifetime cancer risks have been identified as unacceptably high, exceeding the threshold limit of 10−4, while Hazard Quotient (HQ) values above 1 indicate a high non-carcinogenic potential risk, particularly at stations in Khuzestan Province. The Excess Lifetime Cancer Risk (ELCR) values for PM2.5 exposure in the most populated Tehran Province range from 139.4 × 10−6 to 263.2 × 10−6, underscoring significant cancer risks across various monitoring sites. This study emphasizes the urgent need for targeted pollution control measures in each province to effectively mitigate the adverse health effects associated with high PM concentrations.

Impact of Metro Construction Activities on Air Quality: A Case Study of Delhi Region in North India

The current study investigated the concerns about the possible effects of metro infrastructure's fast growth on the environment, particularly air quality. This study investigates how the building of metro lines in Delhi, India, affects the quality of the air at the selected locations in south and west parts of union territory of Delhi. Different air pollutants, including particulate matter (PM10, PM2.5), gaseous pollutants like nitrogen dioxide (NO2), and sulfur dioxide (SO2), were monitored. Air Quality Index (AQI) was also applied to the obtained data to convert the intricate data into single digits. The findings revealed that the values of PM10 and PM2.5 were beyond the National Ambient Air Quality Standards (NAAQS) threshold at the proximity of metro work zones due to construction activity. Moreover, higher NO2 concentrations were noted because of construction machinery operations and vehicle emissions. The study emphasizes the necessity of efficient mitigation solutions, such as green barriers, emission control plans, and dust suppression tactics, to reduce the negative environmental effects of metro development on Delhi's air quality. The results of this study can help urban planners and politicians to create sustainable development plans for transportation infrastructure that protect the environment and public health.

Differential Effects of Various Cleaning Solutions on the Cleaning and Regeneration Performance of Commonly Used Polyester Fiber Material Air Filters

The regeneration and performance of air filter materials are important means of conserving energy and reducing emissions in civil public buildings. The repeated regeneration and use of air filters can not only effectively increase the lifespan of the filters but also reduce the phenomenon of filter disposal and incineration after reaching or not reaching the replacement cycle, further reducing resource waste and air pollution and thereby directly or indirectly reducing carbon emissions. In this study, polyester fiber air filters commonly used in civil public buildings were selected as the research object, and the regeneration performance and structural parameters of water and a cleaning solution were investigated under various cleaning conditions. The results show that the filtration efficiency when cleaning with water was higher than that when cleaning with the cleaning solution. The filtration efficiency for PM10, PM2.5, and PM1.0 increased from 0.3% to 3.5%, 0.7% to 6.3%, and 0.1% to 4.6%, respectively. Water could be used twice for cleaning for PM10 and once for PM2.5, and the cleaning solution could only be used once for cleaning for PM10. The counting filtration efficiency of 0.3 to 2.5 μm particulates showed a relatively significant change. The resistance after cleaning with water was higher than that after cleaning with the cleaning solution. For the quality factor (QF) value of PM10, the cleaning solution had a slightly higher cleaning effect, while for the QF values of PM2.5 and PM1.0, water had a slightly higher cleaning effect. In practical use, it is recommended to first use a cleaning solution and then water for subsequent cleaning. This study provides data support for the use of filters to achieve the dual carbon goal.

PM2.5 Time Series Imputation with Moving Averages, Smoothing, and Linear Interpolation

In this work, a novel model for hourly PM2.5 time series imputation is proposed for the estimation of missing values in different gap sizes, including 1, 3, 6, 12, and 24 h. The proposed model is based on statistical techniques such as moving averages, linear interpolation smoothing, and linear interpolation. For the experimentation stage, two datasets were selected in Ilo City in southern Peru. Also, five benchmark models were implemented to compare the proposed model results; the benchmark models include exponential weighted moving average (EWMA), autoregressive integrated moving average (ARIMA), long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU). The results show that, in terms of average MAPEs, the proposed model outperforms the best deep learning model (GRU) between 26.61% and 90.69%, and the best statistical model (ARIMA) between 2.33% and 6.67%. So, the proposed model is a good alternative for the estimation of missing values in PM2.5 time series.

Exposure of school children to particulate matter and inorganic gaseous pollutants in Hawassa city, Ethiopia.

The detrimental consequences of air pollution on people, particularly vulnerable groups like children, have drawn a lot of attention globally. Studies on the levels and sources of particulate matter (PM2.5 and PM10) and inorganic gaseous pollutants (NO2, CO, and SO2) in primary schools in Africa, including Ethiopia, are scanty. Therefore, the objective of this study was to determine the levels of particulate matter and inorganic gaseous pollutants in primary schools in Hawassa, Ethiopia, and assess potential health risks. The levels of PM2.5 and PM10 were determined using a portable gas monitor device (HoldPeak Laser PM meter, HP 5800D), whereas NO2, CO, and SO2 levels were determined using the Aeroqual Series 500 Portable Air Quality Monitor (Aeroqual Ltd., New Zealand). The levels of PM2.5, PM10, and NO2 in all sampling sites ranged from 11-66.3, 30.8-399.7, and 60.5-152µg/m3, respectively. The levels of PM2.5 and PM10 were found to be above the World Health Organization recommendations in 55% and 85% of the sampling sites, respectively. The hazard quotient (HQ) values for PM10 and PM2.5 ranged from 0.3 to 3.2 and 0.2 to 1.1, respectively. The Air Quality Index (AQI) at 40% and 30% of the outdoor sampling sites was unhealthy for sensitive groups due to exposure to PM2.5 and PM10, respectively. The levels of PM2.5 and PM10, as well as the AQI and HQ values, indicate a poor condition of the air in the schools, especially in the schools near busy traffic roads.

Comprehensive analysis of particulate matter, gaseous pollutants, and microbiological contamination in an international chain supermarket

Indoor environmental quality is of utmost importance since urban populations spend a large proportion of their life in confined spaces. Supermarkets offer a wide range of products and services that are prone to emitting several air pollutants. This study aimed to perform a comprehensive characterisation of the indoor and outdoor air quality in a multinational supermarket, encompassing not only criteria parameters but also unregulated pollutants of concern. Monitoring included measurements of comfort parameters, CO2, multiple gaseous pollutants, particulate matter (PM10) and bioburden. PM10, volatile organic compounds (VOCs) and carbonyls were subject to chemical speciation. Globally, the supermarket presented CO2, VOCs, and PM10 values below the limits imposed by international regulations. The PM10 concentration in the supermarket was 33.5 ± 23.2 μg/m3, and the indoor-to-outdoor PM10 ratio was 1.76. Carbonaceous constituents represented PM10 mass fractions of 21.6% indoors and 15.3% outdoors. Due to the use of stainless-steel utensils, flour and fermentation processes, the bakery proved to be a pollution hotspot, presenting the highest concentrations of PM10 (73.1 ± 9.16 μg/m3), PM10-bound elements (S, Cl, K, Ca, Ti, and Cr) and acetaldehyde (42.7 μg/m3). The maximum tetrachloroethylene level (130 μg/m3) was obtained in the cleaning products section. The highest values of colony-forming units of bacteria and fungi were recorded in the bakery, and fruit and vegetable section. The most prevalent fungal species was Penicillium sp., corresponding to 56.9% of the total colonies. In addition, other fungal species/sections with toxicological or pathogenic potential were detected (Aspergillus sections Aspergilli, Circumdati, Flavi, Mucor and Fusarium sp.).

Seasonal Characteristics of Particulate Matter by Pollution Source Type and Urban Forest Type

To provide consistent air purification benefits from urban forests, it is crucial to identify common characteristics that allow for similar experimental setups. This study aimed to analyze PM10 concentrations in urban forests near pollution sources and understand their mitigation effects. Data from the Asian Initiative for Clean Air Networks, Korea, were used, focusing on three urban forests adjacent to road and industrial pollution sources in Korea, with PM10 concentrations collected during 2021. Considering high PM10 concentrations during winter and spring, these seasons were divided into two sub-periods, resulting in six seasonal periods for analysis. To address the right-skewed PM10 distribution and reduce outlier influence, the Kruskal–Wallis test was used. The results showed that “good” PM10 levels were lowest in early spring, increasing to a peak in summer before declining. High PM10 events were concentrated in spring, early spring, and early winter. The Kruskal–Wallis test indicated lower median PM10 concentrations in urban forests compared to pollution sources in the latter half of the year, while no significant median differences were found in the first half. Distribution visualizations further confirmed that even during high PM10 periods, all urban forests showed lower PM10 values compared to pollution sources. In conclusion, PM10 concentrations in urban forests were consistently lower than in pollution sources across all seasons, demonstrating their effectiveness in air purification at both road and industrial pollution sources. Future research should consider additional variables, such as PM2.5, to further explore differences between pollution sources.

Distribution of air quality data across stratified areas of Port Harcourt metropolis

Distribution of Air Quality Data across seven stratified areas of Port Harcourt Metropolis was carried out. The study utilized a total of 18 parameters from four different air quality datasets obtained from 24 sampling points during field data collection. The sampling points were structured and stratified into residential, industrial, commercial, recreational, transport, dumpsite and abattoir areas. Air quality data were acquired using a multi-digital potable tool, Aerosol Gas Monitor and TESTO Gas Analyser for meteorological data, particulate matters (PM), gaseous emissions and heavy metals. Results showed that the concentrations of air quality data were not evenly distributed across the seven stratified areas, an indication of the influence of location-based activities on the distribution of air quality parameters. The minimum and maximum values of wind speed, temperature and relative humidity were obtained within Residential and Transport Areas, Abattoir and Dumpsite Areas, and, Industrial and Recreational Areas with numerical values of 1.1ms-1 and 3.7ms-1, 27.20C and 350C, and, 67.8% and 923% for wind speed, temperature and relative humidity respectively. The values of TSP, PM2.5 and PM10 ranged from 53 to 255 μg/m3, 13 to 90 μg/m3 and 40 to 169μg/m3 for TSP, PM2.5 and PM10 respectively with the lowest and highest values obtained within Residential and Dumpsite Areas respectively. The concentrations of NO2 and SO2 exceeded the standards of WHO, NAAQS and FMEnv in Industrial, Dumpsite and Abattoir Areas. Lead ( Pb) showed very high values within Dumpsite Area (0.954mg/kg), Industrial Area (0.372mg/kg) and Transport Area (0.077mg/kg).

The Effect of PM10 Pollutant Levels on the Postneonatal Mortality Rate: Application of the AirQ+ Model in Istanbul, Türkiye.

Air pollution is one of the major environmental risk factors for health. Children are vulnerable to the negative health consequences of air pollution. We aimed to determine the effect of PM10 levels on postneonatal mortality in Istanbul, the most populous city in Türkiye. In this cross-sectional study, the relationship between PM10 levels and postneonatal deaths occurring in Istanbul, Türkiye in 2015-2019 was examined. PM10 levels for Istanbul were calculated by taking the average of daily PM10 measurements between 01.01.2015 and 31.12.2019, made available from Istanbul Air Quality Monitoring Stations. Data were analyzed using Microsoft Office Excel 2016 and AIRQ+ 2.2.3 software. If the PM10 value in Istanbul province had been reduced to 20 μg/m3, the limit value recommended by the WHO; in 2019; 36(19-61) postneonatal infant deaths could have been prevented; 7.73% (3.98-12.95) of postneonatal infant deaths were attributed to PM10. During this period, the PM10 value in Istanbul was above the limit value recommended by WHO, the European Union and Turkish legislation. Infant mortality due to air pollution is an important public health problem.

Classroom indoor air quality and noise level assessment of different educational institutions in a university area in Bangladesh

Introduction: The classroom environment is crucial for fostering effective learning and safeguarding teacher-student health. This study assessed the Indoor Air Quality (IAQ) and noise levels in classrooms across three institutions: a university, a secondary and higher secondary school (called a school and college), and a primary school. Materials and methods: Various IAQ parameters such as Particulate Matters (PM2.5 and PM10), Carbon monoxide (CO), Carbon dioxide (CO2), Total Volatile Organic Compound (TVOC), and temperature, Relative Humidity (RH), light, and noise levels were measured using calibrated instruments from February to March 2024. Results: The air pollutants and noise levels varied among the institutions. The mean values of PM 2.5 (76.6 µg/m3) and PM (116.7 µg/m3) were highest The mean values of PM 2.5 (76.6 µg/m3) and PM (116.7 µg/m3) were highest in the primary school, while CO (0.82 ppm), light (92.9 lux), temperature (27.6 °C), and noise levels (77.6 dB) peaked in the school and college. University classrooms showed the maximum concentrations of CO2 (804.9 ppm), TVOC (32.9 ppb), and RH (58.6%). In all institutions, PM2.5, PM10, and noise levels exceeded WHO-recommended limits, whereas CO, CO2, RH, and temperature remained within their respective standards. Light levels were below Occupational Safety and Health Administration (OSHA) guidelines. Correlation analysis showed significant positive correlations between PM2.5 PM10, and CO. Hazard Quotient (HQ) values for PM 2.5 and PM10 exceeded 1.0, indicating potential health risks. Variations in pollutants and noise levels among institutions may be due to classroom facilities, student density, ventilation systems, and student activities. Conclusion: Long-term exposure to IAQ pollutants and noise levels could impair cognitive function, respiratory health, and overall well-being of the students and educators. Implementing proper ventilation (e.g., HEPA filters) systems, soundproofing acoustic panels, and continuous IAQ monitoring is recommended for a safer classroom environment.

Air Pollutant Emission Factors of Inland River Ships under Compliance

Inland river ships (IRSs) use diesel with a lower sulfur content and emit relatively low emissions, making it challenging to monitor their emissions. Sniffer monitoring equipment was installed from August 2020 to June 2022 at the Gezhou Dam of the Yangtze River and monitored emissions from 8,238 IRSs passing through the lock. We partnered with the maritime department to select 100 ships passing through the lock to extract fuel oil samples for direct fuel sulfur content (FSC) detection, which determined the actual FSC of the passing ships. The monitoring data from these 100 ships indicated that the relative error of the SO2 emission factors (EFs) and FSC results is significant at the 10-parts-per-million level. The monitoring data from the remaining 8,138 ships showed that the EFs of NO, NO2, PM2.5, and PM10 were 24.02 ± 16.92 g kg−1, 10.30 ± 18.08 g kg−1, 0.72 ± 0.60 g kg−1, and 0.92 ± 0.70 g kg−1, respectively. The NOx EFs of container ships are higher than those of other ship types, while the PM EFs for different ship types do not significantly differ. Based on these EFs, we calculated the average emission rates for different types of ships passing through locks, which is a real-time measurement method for estimating ship emissions. In addition, a comparison of ship EF measurements over the past 20 years revealed that EF values for SO2, NOx, and PM exhibited a downward trend, with the calculated results of the current study determined to be the lowest numerical level.

Impact of construction activities on the air quality of Agra city, Uttar Pradesh, India

The current research work was carried out to comprehensively assessed the impact of construction activities on the air quality. Seven sites were chosen along the metro line construction in Agra, Uttar Pradesh, India, to meet the study's goals. The monitoring was performed for 24 hours at each site using the respirable dust sampler (RDS) with a gaseous sampling attachment. The raw data was processed to calculate the Air Quality Index (AQI). The data obtained indicate that all the examined sites had PM10 (particulate matter having the diameter less than or equal to 10 micron) values above the National Ambient Air Quality Standards (NAAQ) values of 100 µg/m3, while SS-05 and SS-06 had PM2.5 (particulate matter having the diameter less than or equal to 2.5 micron) values above the NAAQ values of 60 µg/m3. Values of CO, SO2, and NO2 were discovered to be lower than the NAAQ standard limits. Because PM10's sub index (Si) was found to be the greatest across all locations, it was determined to be the criterion pollutant among all the metrics. Based on the AQI value, the research area's overall air quality was determined to be moderately polluted. At every location, a variety of management techniques, including mist guns, water spraying, and planting, are regularly used to reduce air pollution. Effective implementation of applied air pollution control measures is required to make the air clean and safe for breathing.

FUGITIVE DUST MONITORING AT UNDER CONSTRUCTION ROADS WITH AND WITHOUT WATER SPRINKLING IN LAHORE CITY

This study investigates the effectiveness of water sprinkling in reducing PM10 levels in Lahore, Pakistan. The data collected over a period of several days shows that water sprinkling significantly reduces PM10 concentrations in the air. The average PM10 value before water sprinkling was consistently above the PEQS, indicating poor air quality. However, after water sprinkling, the average PM10 value decreased by 49%, demonstrating a substantial improvement in air quality. The most notable reduction in PM10 levels was observed during the immediate hours following water sprinkling, suggesting that its effectiveness is most pronounced in the short term. However, the reduction gradually decreased over time as the dust particles settled and the moisture evaporated. While water sprinkling is a viable method for reducing fugitive dust pollution, it is important to consider its limitations and potential drawbacks. Excessive water use can lead to waterlogging and other environmental issues, and its effectiveness may be limited during dry seasons or in regions with limited water resources. Therefore, a combination of control measures, including proper wetting of dusty areas, covering stockpiles of materials, and using dust suppressants, is essential for achieving long-term improvements in air quality.

Analysis of PM10 Substances via Intuitionistic Fuzzy Decision-Making and Statistical Evaluation

Air pollution is a situation that negatively affects the health of humans and all living things in nature and causes damage to the environment. The most important cause of air pollution is the amount and density of substances called “particulate matter” above guidelines. Particulate matter (PM) are mixed liquid droplets and solid particles with advective diameters less than 2.5 μm (PM2.5—fine particles) and between 2.5 and 10 μm (PM2.5–10—coarse particles). PM10 is defined as one that can remain in the air for a long time and settle in the respiratory tract, damaging the lungs. It is important to identify the underlying causes of air pollution caused by PM10. In this context, these criteria need to be evaluated to minimize the negative effects of PM10. In the study, monthly average PM10 data obtained from the Air Quality Monitoring Station in Kocaeli, Türkiye, between 2017 and 2023 are used. After determining the criteria for PM10, the criteria are prioritized with the Intuitionistic Fuzzy AHP (IF-AHP) method by taking decision-maker opinions. The proposed decision-making model aims to guide obtaining and focusing on the important causes of out-of-limit and dangerous PM10 concentrations in the air. Additionally, PM10 data is analyzed in the context of COVID-19 and a statistical analysis is conducted. One-way Analysis of Variance (ANOVA) is used to evaluate whether there is a significant difference in average monthly data over the years. The Games–Howell test, one of the post-hoc tests, is used for determining differences between groups (years). In addition, monthly PM10 values for the future are estimated using the Expert Modeler tool in the software IBM® SPSS® Statistics 22. The study is important in that it provides a focus on the criteria affecting PM10 with an intuitionistic fuzzy perspective, along with statistical analysis.

Impact of short-term exposure to ambient air pollutants and meteorological factors on COVID-19 incidence and mortality: A retrospective study from Dammam, Saudi Arabia

The symptoms of COVID-19 included fever with or without respiratory syndrome, but patients subsequently developed pulmonary abnormalities. Exposure to air pollution, meanwhile, is associated with complications such as acute respiratory inflammations, asthma attack, and deaths from cardiorespiratory disease. To analyze the association of the air quality index (AQI), ambient air pollutants (PM10, SO2 and O3) and meteorological parameters (temperature and relative humidity [RH]) with COVID-19 incidence and mortality, a retrospective study was conducted to examine COVID-19 infection, meteorological parameters, ambient air quality and ambient air pollutants in Dammam from 1 January to 30 April 2021. Data of COVID-19 incidence and mortality for Dammam were retrieved from Saudi Arabia Ministry of Health's publicly accessible database. Meteorological data, AQI and average PM10, SO2 and O3 values were extracted from the publicly available website of Ministry of Environment, Water and Agriculture. The correlation of COVID-19 incidence and mortality with the independent variables was analysed by Pearson's correlation test or Spearman's rho test as applicable, and a p-value less than 0.05 was considered significant. COVID-19 incidence exhibited a positive correlation with temperature (r = 0.537, p = .0001) and a negative correlation with RH (r=−0.487, p=.0001). No correlation was observed between the meteorological variables and COVID-19 mortality. COVID-19 incidence showed a positive correlation with AQI (r=0.269, p=.015) and with the ambient air pollutants SO2 and O3 (r=0.258, p=.018), and COVID-19 mortality showed a positive correlation with PM10 (rs = 0.344, p=.002). Short-term exposure to O3, SO2 and higher temperature had direct relationship with COVID-19 incidence, while RH had inverse relationship. PM10 is positively associated with COVID-19 mortality.

Effects of Meteorological Conditions in Summer and Early Autumn on PM2.5 and O3 Pollution in Beijing-Tianjin-Hebei and Surrounding Areas

PM2.5 pollution remains prominent in autumn, whereas O3 pollution gradually manifests in summer. To understand the dual high characteristics and meteorological effects of PM2.5 and O3 in the summer and early autumn of 2021 in the Beijing-Tianjin-Hebei and surrounding areas, the spatiotemporal distribution characteristics of PM2.5 and O3 concentrations, as well as meteorological conditions, subtropical high index, and weather situation in the Beijing-Tianjin-Hebei and surrounding areas were analyzed. The results showed that PM2.5 concentration and DPO3 （O3 daily maximum 8h mean minus O3 concentration at 06：00） from June to September 2021 decreased compared with those in the same period in 2020 and 2022, and high concentrations were mainly occurring in June. The overall PM2.5 concentration and DPO3 showed a gradual decrease from the middle to the north and south, with synchronous spatiotemporal changes. The hourly value of PM2.5 concentration presented a multimodal distribution, reaching the peak at 07：00-10：00 and 22：00-24：00. O3 concentration showed an opposite trend of change with PM2.5 concentration, reaching their peak from 14：00-16：00. When DPO3 and the concentration of PM2.5 were high, the characteristics of near-surface meteorological elements were characterized by temperatures ranging from 24.0-28.0℃, relative humidity concentrated at 58.4%-76.3%, and wind speeds ranging from 1.5-3 m·s-1. There was a high lag correlation between the subtropical high index and DPO3. When the subtropical high was farther and stronger from the Beijing-Tianjin-Hebei and surrounding areas, DPO3 was higher. The major weather patterns with both high PM2.5 and O3 concentrations in the study area were near surface low-pressure front, high-pressure type, and frontal type. The high altitude was controlled by high-pressure ridges, and the subtropical high ridge line was stable between 21°-28°N.

High resolution mapping of nitrogen dioxide and particulate matter in Great Britain (2003–2021) with multi-stage data reconstruction and ensemble machine learning methods

In this contribution, we applied a multi-stage machine learning (ML) framework to map daily values of nitrogen dioxide (NO2) and particulate matter (PM10 and PM2.5) at a 1 km2 resolution over Great Britain for the period 2003–2021. The process combined ground monitoring observations, satellite-derived products, climate reanalyses and chemical transport model datasets, and traffic and land-use data. Each feature was harmonized to 1 km resolution and extracted at monitoring sites. Models used single and ensemble-based algorithms featuring random forests (RF), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), as well as lasso and ridge regression. The various stages focused on augmenting PM2.5 using co-occurring PM10 values, gap-filling aerosol optical depth and columnar NO2 data obtained from satellite instruments, and finally the training of an ensemble model and the prediction of daily values across the whole geographical domain (2003–2021). Results show a good ensemble model performance, calculated through a ten-fold monitor-based cross-validation procedure, with an average R2 of 0.690 (range 0.611–0.792) for NO2, 0.704 (0.609–0.786) for PM10, and 0.802 (0.746–0.888) for PM2.5. Reconstructed pollution levels decreased markedly within the study period, with a stronger reduction in the latter eight years. The pollutants exhibited different spatial patterns, while NO2 rose in close proximity to high-traffic areas, PM demonstrated variation at a larger scale. The resulting 1 km2 spatially resolved daily datasets allow for linkage with health data across Great Britain over nearly two decades, thus contributing to extensive, extended, and detailed research on the long-and short-term health effects of air pollution.

Spatio-temporal distribution and source contributions of the ambient pollutants in Lucknow city, India.

Clean air is imperative to the survival of all life forms on the planet. However, recent times have witnessed enormous escalation in urban pollution levels. It is therefore, incumbent upon us to decipher measures to deal with it. In perspective, the present study was carried out to assess PM10 and PM2.5 loading, metallic constituents, gaseous pollutants, source contributions, health impact and noise level of nine-locations, grouped as residential, commercial, and industrial in Lucknow city for 2019-21. Mean concentrations during pre-monsoon for PM10, PM2.5, SO2 and NO2 were: 138.2 ± 35.2, 69.1 ± 13.6, 8.5 ± 3.3 and 32.3 ± 7.4µg/m3, respectively, whereas post-monsoon concentrations were 143.0 ± 33.3, 74.6 ± 14.5, 12.5 ± 2.1, and 35.5 ± 6.3µg/m3, respectively. Exceedance percentage of pre-monsoon PM10 over National Ambient Air Quality Standards (NAAQS) was 38.2% while that for post-monsoon was 43.0%; whereas corresponding values for PM2.5 were 15.2% and 24.3%. Post-monsoon season showed higher particulate loading owing to wintertime inversion and high humidity conditions. Order of elements associated with PM2.5 is Co < Cd < Cr < Ni < V < Be < Mo < Mn < Ti < Cu < Pb < Se < Sr < Li < B < As < Ba < Mg < Al < Zn < Ca < Fe < K < Na and that with PM10 is Co < Cd < Ni < Cr < V < Ti < Be < Mo < Cu < Pb < Se < Sr < Li < B < As < Mn < Ba < Mg < Al < Fe < Zn < K < Na < Ca. WHO AIRQ + ascertained 1654, 144 and 1100 attributable cases per 0.1 million of population to PM10 exposure in 2019-21. Source apportionment was carried out using USEPA-PMF and resolved 6 sources with highest percent contributions including road dust re-entrainment, biomass burning and vehicular emission. It is observed that residents of Lucknow city regularly face exposure to particulate pollutants and associated constituents making it imperative to develop pollution abetment strategies.

Comparison of the polluted dust plume and natural dust air mass in a spring dust event in Beijing 2023

The occurrence of extreme weather events is becoming more frequent due to global climate change. A long-lasting dust outbreak in the spring of 2023 was triggered by Mongolia cyclones and cold fronts in the dust source areas. In this study, we illustrate the spatial distribution, the transport path of the dust and its influence on the air quality of downstream cities utilizing ground-based and space-borne measurements. Results indicate a more complicated pollution, coexisting of polluted dust stage S1 and pure dust stage S2. In S1, the aerosol was characterized by a dual-layer vertical structure—high extinction coefficient of nearly 1.0 km−1 with a low particle depolarized ratio (PDR) of < 0.1 under 500 m, and a small extinction coefficient of 0.3 km−1 with a high PDR of 0.15 above 500 m. Then the intrusion of the Mongolian cyclone carried new dust plumes on 10 March, suggesting the onset of pure dust period S2. The source transition was also confirmed by the MODIS, CALIPSO and Hysplit observations. The pure Asian dust evolved rapidly with one thickening layer and distributed homogeneously in the boundary layer. The PDR increased significantly to the peak of 0.35 and resulted in the peak PM10 value of > 1300 µg/m3. PM10 positively correlated with trace gases in S1 while varying inversely with the pollution gases in S2. The results help to shed light on the classification of different types of dust and also be useful in developing an effective strategy to forecast air pollution in downstream areas.