Average Concentrations Of PM2 Research Articles

Identification of particulate matter (PM10 and PM2.5) sources using bivariate polar plots and k-means clustering in a South American megacity: Metropolitan Area of Lima-Callao, Peru.

The identification of different air pollution sources is essential to effectively control atmospheric pollution, particularly in megacities of emerging countries with rapid economic development, such as the Metropolitan Area of Lima-Callao (MALC). The objective of this research was to identify the main sources of particulate matter pollution by applying bivariate polar plots and the k-means clustering algorithm. These statistical techniques were applied to hourly in situ data of four variables collected over a 5-year period (2015-2019) by the Automatic Air Quality Monitoring Network of the MALC: wind direction, wind speed, PM10, and PM2.5 concentrations. Average PM10 concentrations ranged from 34μgm-3 (CDM station) to 126.7μgm-3 (VMT station), while average PM2.5 concentrations ranged from 16.8μgm-3 (CDM station) to 41.2μgm-3 (ATE station). The diurnal variation of PM presented two peaks, one in the morning (from 0800 to 1000h) and the other at night (from 1900 to 2300h), with the highest concentrations of PM10 recorded at the ATE (0800h: 155.8μgm-3) and VMT (2100h: 154.6μgm-3) stations, and PM2.5 at the ATE station (0800h: 60.3μgm-3 and 2300h: 37.5μgm-3). The results showed that the contributions of PM10 are directly related to emissions from industrial activities, automotive fleet, construction, demolition, wind erosion, and the suspension and resuspension of particulates from unpaved roads. Meanwhile, high concentrations of PM2.5 are mainly attributed to vehicle exhaust emissions, industrial emissions, secondary particulate formation, and drag by the action of the winds. The major source of particulate matter contamination is the vehicle fleet, and within this, automobiles, station wagons, combi vans, and 2 and 3-wheel motorcycles are those that have the greatest contribution. These results were supported by non-parametric statistical tests such as Kruskal-Wallis and Mann-Whitney U and validated by the conditional bivariate probability function. The findings of this work may help to implement pollution prevention and control strategies in the future within this South American megacity.

Seasonal trend analysis and influence of meteorological factors on the concentration of atmospheric pollutants in an urban area

Introduction: Air pollution is a growing concern in Greater Noida, Uttar Pradesh, where meteorological factors play a major role. As urbanization accelerates, the region has not yet thoroughly examined the impact of factors like temperature, humidity, wind direction, and speed on pollutants like Particulate Matters (PM2.5, PM10), Nitrogen dioxide (NO2), and Sulfur dioxide (SO2) Materials and methods: To explore this issue, a 12-month study (January to December 2023) was conducted to analyze the relationships between these meteorological factors and pollutant levels. In the present study, pollutant movement was tracked over time. Pearson correlation was used to correlate air pollution concentrations with weather conditions. Results: The study revealed that the average concentrations of PM , PM , 10 2.5 and NO2 were 912.54 µg/m³, 257.42 µg/m³, and 65.98 µg/m³, respectively. In contrast, SO2 had an average concentration of 26.85 µg/m³. Conclusion: The study indicates that particulate matter and temperature are strongly negatively correlated, while wind speed shows a weak positive correlation with particulate matter. Pollution levels and relative humidity also display a negative correlation, as do particulate matter concentrations and humidity. However, gaseous pollutants exhibit little to no significant correlation with these weather conditions

Association Between Fine Particle Waves and Sexual Function: A Nationwide Cross-Sectional Survey in China.

The effect of the long-term persistently elevated air pollutants, often referred to as air pollution waves, on sexual function has not been sufficiently addressed. This nationwide cross-sectional study involved 12,157 participants, with 5496 females and 5039 males. PM waves were characterized by daily average PM concentrations surpassing Grade II thresholds of China's ambient air quality standards (PM2.5 > 75 μg/m3, PM10 > 150 μg/m3) for three or more consecutive days (3-8 days). Male sexual function was assessed through the International Index of Erectile Function-5 (IIEF-5) and the Premature Ejaculation Diagnostic Tool (PEDT), while female sexual function was evaluated using the Female Sexual Function Index (FSFI). A multivariate linear regression model was employed to investigate the link between PM wave exposure and sexual function. Exposure to PM10 waves, defined as 3 (β = -0.0145, 95%CI = -0.0280, -0.0010), 4 (β = -0.0145, 95%CI = -0.0280, -0.0010), 5 (β = -0.0193, 95%CI = -0.0371, -0.0015), 6 (β = -0.0218, 95%CI = -0.0415, -0.0021), 7 (β = -0.0243, 95%CI = -0.0458, -0.0028), and 8 (β = -0.0243, 95%CI = -0.0458, -0.0028) consecutive days, negatively impacted IIEF-5 scores and male sexual function. Moreover, depression levels, as evaluated by the PHQ-9, played a mediating role in the connection between PM10 waves and IIEF-5 scores. The potentially vulnerable subgroups were the younger 20-30 and the low-income groups. Our results suggest for the first time that PM10 waves are associated with decreased IIEF-5 scores, which are mediated by depression score PHQ-9, informing policy formulation for public health interventions and individual safeguarding.

Carbonaceous aerosol particle sources in Manila North Port and the urban environment

This study addresses the pressing issue of black carbon (BC) pollution in urban areas, focusing on two locations in the Philippines: Quezon City’s East Avenue (QCG, roadside urban environment) and Manila’s North Port. We found that organic aerosol particles (OA) made a greater contribution (80%) to total submicron particulate matter compared to inorganic aerosol (IA) (20%). The mean hourly average equivalent black carbon (eBC) mass concentration at the QCG site (35.97 ± 16.20 𝜇g/m3) was noticeably higher compared to the Port (10.27 ± 5.99 𝜇g/m3), consistent with trends in other Asian cities. Source apportionment analysis identified eBC related to transport emissions (eBCTR) as the predominant contributor to eBC, accounting for 86% at the Port and 80% at QCG. Diurnal patterns showed the highest eBCTR mass concentrations (47.69 ± 9.34 𝜇g/m3) during morning rush hours, which can be linked to light-duty vehicles. Late-night (10 pm–12 am) high concentrations (30.63 ± 8.45 𝜇g/m3) can be associated with heavy diesel trucks at the QCG site. Whereas at the Port site, hourly average higher eBCTR concentration (12.24 ± 3.65 𝜇g/m3) during morning hours (6 am–8 am) can be attributed to the traffic of heavy-duty trucks, trollers, diesel-powered cranes and ships. Compared to the QCG site, a lower eBC concentration at the Port site was favoured by the more open environment and higher wind speed, facilitating better pollutant dispersion. The mean hourly average concentrations of PM2.5 and PM10, measured using an Aerodynamic Particle Sizer, consistently exceeded the air quality standards set by the World Health Organization and the Philippine Clean Air Act at both sites. This study highlights the persisting BC pollution in developing regions and calls for scientifically based strategies to mitigate the air quality crisis.

Demonstrating multi-benefits of green infrastructure to schools through collaborative approach.

Green infrastructure (GI) is known to reduce road air pollution exposure, but their implementation in schools and associated benefits remain under-researched. In this study, two GI solutions, green screen and green gate, were co-designed and installed at a primary school in Guildford using collaborative and participatory methods. By assessing changes in air pollution levels, noise, and public perception before and after GI installation, we aimed to understand their impact on reducing children's exposure and evaluate other co-benefits. Without considering wind direction's effect, a maximum reduction of up to 32%, 10% and 12% in the average daily concentration of PM10 (green gate), PM2.5 (green screen) and PM1 (green gate), respectively, when compared with in-front concentration. The decay in concentration decreases with distance from the GI, and different wind directions result in varying percentage reductions in PM concentration. For the green screen, 'parallel to the screen' and for the green gate, 'away from the gate' wind directions provided the highest PM reduction. The horizontal abatement efficiency of GI varied with PM size, with the highest being PM10. Continuous monitoring behind the green screen revealed a decrease in PM concentration after installation, and this relative concentration varied from 0.29 to 0.90 compared to before installation. The green gate effectively lowered noise by 5dB(A), and the green screen did not report a noticeable impact on noise levels. Most parents perceived the installation of GI in school as significantly decreasing air pollution exposure and slightly reducing noise levels, resembling the changes in their levels observed in monitoring. The successful co-creation and co-implementation of GI interventions and resulting co-benefits underscore the importance of community engagement and participatory approaches in urban planning and environmental management. This study paves the way for the wider-scale application of innovative strategies involving local communities, stakeholders, and policymakers in implementing GI projects to ensure their sustainability and effectiveness.

Landscape Metrics as Ecological Indicators for PM10 Prediction in European Cities

Despite significant progress in recent decades, air pollution remains the leading environmental cause of premature death in Europe. Urban populations are particularly exposed to high concentrations of air pollutants, such as particulate matter smaller than 10 µm (PM10). Understanding the spatiotemporal variations of PM10 is essential for developing effective control strategies. This study aimed to enhance PM10 prediction models by integrating landscape metrics as ecological indicators into our previous models, assessing their significance in monthly average PM10 concentrations, and analyzing their correlations with PM10 air pollution across European urban landscapes during heating (cold) and non-heating (warm) seasons. In our previous research, we only calculated the proportion of land uses (PLANDs), but according to our current research hypothesis, landscape metrics have a significant impact on PM10 air quality. Therefore, we expanded our independent variables by incorporating landscape metrics that capture compositional heterogeneity, including the Shannon diversity index (SHDI), as well as metrics that reflect configurational heterogeneity in urban landscapes, such as the Mean Patch Area (MPA) and Shape Index (SHI). Considering data from 1216 European air quality (AQ) stations, we applied the Random Forest model using cross-validation to discover patterns and complex relationships. Climatological factors, such as monthly average temperature, wind speed, precipitation, and mean sea level air pressure, emerged as key predictors, particularly during the heating season when the impact of temperature on PM10 prediction increased from 5.80% to 22.46% at 3 km. Landscape metrics, including the SHDI, MPA, and SHI, were significantly related to the monthly average PM10 concentration. The SHDI was negatively correlated with PM10 levels, suggesting that heterogeneous landscapes could help mitigate pollution. Our enhanced model achieved an R² of 0.58 in the 1000 m buffer zone and 0.66 in the 3000 m buffer zone, underscoring the utility of these variables in improving PM10 predictions. Our findings suggest that increased urban landscape complexity, smaller patch sizes, and more fragmented land uses associated with PM10 sources such as built-up areas, along with larger and more evenly distributed green spaces, can contribute to the control and reduction of PM10 pollution.

ANALISIS KUALITAS GAS BUANG PADA STEAM BOILER BERBAHAN BAKAR BIO-SOLAR DAN BATU BARA

Steam boilers fueled by biodiesel and coal are one of the industrial activities that still produce exhaust emissions such as Total Particulate (PM), Sulfur Dioxide (SO2), Opacity, Nitrogen Oxide (NOx), and Carbon Monoxide (CO) which is very dangerous for public health and the environment. Therefore, analysis of flue gas emissions in steam boiler installations equipped with filtration devices (cyclones, water scrubbers, and electrostatic precipitators) before being discharged into the environment is of important. Exhaust gas emission was measured by using a gas analyzer, smoke tester, and portable quality air monitor. The results of exhaust gas emission analysis showed that the average concentration of PM, SO2, and CO did not meet the specified emission quality standard thresholds, exempted for opacity concentration for both steam boilers fueled with biodiesel and coal. It is suspected that this is caused by the sub optimal performance of the filtration equipment used in this system. Therefore, regular maintenance is required, and if necessary, a redesign of the entire system can be carried out.

Potential Strategies to Improve Air Quality in Tirana for Current and Future Generations

Tirana, the capital of Albania, faces severe air pollution, posing significant health risks and environmental concerns for its residents. Referring to the data for the year 2023, KTA admits that Tirana exceeds the European Union norms for PM10 and nitrogen dioxide. Annual average concentrations of PM10 and NO2 in Tirana are above the limit values of National Air Quality Standard and World Health Organization. This paper looks at the several issues that lead to Tirana's air pollution and considers ways to lower pollution levels and enhance air quality. The problems with Tirana's air pollution are intricate and varied, resulting from a confluence of elements including as topography, industrial processes, burning of biomass, vehicular emissions, and insufficient monitoring programs. Addressing these challenges requires regulatory interventions, technological innovations, public awareness campaigns and stakeholder engagement to improve air quality. Tirana has a population of 528,000 inhabitants, with a surface area of 40 km² and a population density of 13.2 thousand inhabitants. Tirana has the capacity to develop more resilient, sustainable and habitable urban areas. To realize innovative ideas, enhance green areas, and improve air quality for present and future generations, cooperation amongst urban planners, legislators, community organizations, and citizens is imperative. We identify critical actions for policy makers, urban planners, and stakeholders to mitigate air pollution and build a healthier and more sustainable urban environment in Tirana based on pollution sources, regulatory frameworks, and ongoing initiatives. This paper's goal is to offer solutions for enhancing air quality while advancing environmental sustainability and public health. Received: 2 August 2024 / Accepted: 10 November 2024 / Published: 3 December 2024

Specifying the Associations between PM2.5 Constituents and Gastrointestinal Cancer Incidence: Findings from a Prospective Cohort Study in Beijing, China.

This study aimed to test the association between PM2.5 and the incidence of gastrointestinal (GI) cancer, and further to detect the primary constituents on this association. A sum of 142,982 participants without GI cancer at baseline were derived from the National Urban Cancer Screening Program in Beijing (2013-2019). The 5 year averaged concentrations of PM2.5 mass and its five constituents, namely, black carbon (BC), ammonium (NH4+), nitrate (NO3-), organic matter (OM), and inorganic sulfate (SO42-), were estimated by using a hybrid machine learning model. The Cox proportional hazard model with fixed effects was used to explore the associations between PM2.5 mass and its constituents with the incidence of GI cancer. The double-exposure linear model, the mixture exposure model of quantile-based g-computation, and an explainable machine learning model were utilized to evaluate the importance of different PM2.5 constituents. Long-term exposure to PM2.5 mass and its constituents was linearly associated with GI cancer; the estimated hazard ratio and 95% confidence interval (95% CI) of per standard deviation increment were 1.367 (95% CI: 1.257 to 1.487) for PM2.5 mass, 1.434 (95% CI: 1.307 to 1.574) for BC, 1.255 (95% CI: 1.169 to 1.349) for NH4+, 1.217 (95% CI: 1.139 to 1.301) for NO3-, 1.410 (95% CI: 1.287 to 1.546) for OM, and 1.410 (95% CI: 1.288 to 1.542) for SO42-. By using multiple methods, results indicated that SO42- and BC were the most important constituents. Results indicated that long-term exposure to PM2.5 was associated with a high incidence of GI cancer, and BC and SO42- were robustly identified as the primary constituents.

Impact of ambient air pollution on hospital visits for mental and behavioral disorders among residents in an industrial area in Henan Province from 2016 to 2021

Objective: To explore the impact of air pollution on hospital visits for mental and behavioral disorders among residents in an industrial area in Henan Province from 2016 to 2021. Methods: Daily outpatient visits data for mental and behavioral disorders, as well as air pollutants and meteorological data during the same period, were collected from Angang General Hospital in Angang Industrial Zone at Anyang City between January 2016 and December 2021. A generalized additive model was used for time-series analysis to examine the relationship between daily average concentrations of nitrogen dioxide (NO2), sulfur dioxide (SO2), fine particulate matter (PM2.5), inhalable particulate matter (PM10), carbon monoxide (CO), and ozone (O3) with a lag of 0 to 7 days on the number of visits for mental and behavioral disorders among residents. The single-day lag effect (lag0-lag7 d) and cumulative lag effect (lag01-lag07 d) were analyzed. The smooth cubic spline function was used to fit the exposure-response relationship, and subgroup analysis was performed according to different genders, seasons and ages. Results: A total of 26 268 hospital visits for mental and behavioral disorders were collected from the industrial area between 2016 and 2021. The daily average concentrations of SO2, NO2, PM2.5, PM10, and CO were (27.50±27.33), (43.11±18.33), (73.87±60.30), (134.01±83.81) μg/m3, and (1.72±1.03) mg/m3, respectively. The daily maximum 8-hour average concentration of O3 was (82.18±53.70) μg/m3. After controlling for long-term trends, temperature, relative humidity, day of the week effect, and holiday effects, the generalized additive model analysis showed that NO2 had a statistically significant impact on the hospital visits for mental and behavioral disorders at lag0 d, lag2 d and lag01-lag05 d and CO had a statistically significant impact at lag0-lag3 d and lag01-lag06 d (all P<0.05). NO2 at lag02-lag04 d and CO at lag0-lag2 d and lag01-lag04 d had statistically significant effects on the visits for neurasthenia (both P<0.05). The impacts of NO2 at lag03-lag04 d, PM2.5 at day lag3 d and lag03-lag04 d, PM10 at lag3 d and lag03 d, and CO at lag3 d and lag01-lag05 d on visits for generalized anxiety disorder were also statistically significant (all P<0.05). After false discovery rate (FDR) correction, it was shown that for every 10 μg/m3 increase in NO2 and every 0.1 mg/m3 increase in CO, the percentage increase in visits for mental and behavioral disorders and its 95% confidence interval (95%CI) were 3.38% (0.95%-5.87%) and 0.78% (0.38%-1.17%), respectively. For every 0.1 mg/m3 increase in CO, the visits for neurasthenia increased by 0.78% (0.27%-1.29%). For every 10 μg/m3 increase in PM2.5 and every 0.1 mg/m3 increase in CO, the visits for generalized anxiety disorder increased by 1.07% (0.46%-1.68%) and 1.17% (0.37%-1.97%), respectively (adjusted P<0.05). There was a linear exposure-response relationship between NO2 and CO and the hospital visits for mental and behavioral disorders, CO and the hospital visits for neurasthenia, and CO and PM2.5 and the hospital visits for generalized anxiety disorder (P<0.05 for the overall association test and P>0.05 for the non-linearity test). Stratified analysis showed that air pollutants had an impact on male patients with neurasthenia, female patients with generalized anxiety disorder, individuals aged<45 years with mental and behavioral disorders, and individuals aged≥65 years with generalized anxiety disorder. The impact of air pollutants was greater during the cold season or winter. Conclusion: Exposure to air pollution can increase hospital visits for mental and behavioral disorders among residents in industrial areas, with a higher risk among those aged<45 years old and during the cold season.

Characterization of the Elemental Composition of Aerosols Emitted in the Dry Season of the Pantanal Wetland, Brazil

The Brazilian Pantanal region experiences intense biomass burning during the dry season, releasing large quantities of gasses and particles into the atmosphere, which have serious implications on both the climate system and public health. Understanding the dynamics of these emissions is crucial for mitigating the impact on the ecosystem, its functioning, and potential anthropogenic disturbances. This study focused on analyzing emissions in the northern Pantanal during the 2022 drought. Concentrations of fine particulate matter (PM2.5), black carbon (BC), and 25 chemical elements were measured using gravimetry, reflectance analysis, and X-Ray fluorescence, respectively, from samples collected between August and October 2022. The average concentrations of PM2.5 and BC increased approximately 4-fold and 2.5-fold, respectively, compared to averages from a decade ago. Significant increases were also observed in elements such as sulfur (S), potassium (K), iron (Fe), and silicon (Si). The maximum concentrations were comparable to values typical of the southern Amazon, a region known for high deforestation rates and land use changes. Elemental analysis revealed substantial shifts in concentrations, primarily associated with biomass burning (BB) and soil suspension. Additionally, enrichment factor (Ef) analysis showed that lead (Pb) levels, correlated with human activities, were 200 times higher than those found under clean atmospheric conditions.

A comparison of statistical and machine learning models for spatio-temporal prediction of ambient air pollutant concentrations in Scotland

The spatio-temporal prediction of air pollutant concentrations is vital for assessing regulatory compliance and for producing exposure estimates in epidemiological studies. Numerous approaches have been utilised for making such predictions, including land use regression models, additive models, spatio-temporal smoothing models and machine learning prediction algorithms. However, relatively few studies have compared the predictive performance of these models thoroughly, which is one of the novel contributions of this paper. For the specific challenge of predicting monthly average concentrations of NO2, PM10 and PM2.5 in Scotland, we find that random forests typically outperform (or are as good as) more traditional statistical prediction approaches. Additionally, we utilise the best performing model to provide a new data resource, namely, predictions of monthly average concentrations (with uncertainty quantification) of the above pollutants on a regular 1 km2 grid for all of Scotland between 2016 and 2020.

A Study on Ambient Air Quality and Management inCommercial Areas of Gorakhpur City

The current study evaluates the city of Gorakhpur's ambient air quality using an air quality index. For the period January 2018 to November 2022, the 24-hour average concentration of the three primary pollutants SO2, NO2, andPM10 in a commercial area close to Jalkal Bhawan in Gorakhpur city was measured. Pollutant measurements were made between January 2018 and November 2022. The IND-AQI technique was implemented to calculate the Air Quality Index. In July, the highest monthly average concentrations of PM10, SO2, and NO2 were recorded at 401.13, 36.41, and 54.02, respectively. The yearly average SO2 concentration was found to be within the CPCB- recommended limit, while NO2 was found to be slightly above the maximum allowable level. However, PM10 wasconsistently found to be above the CPCB-recommended limit. The average monthly concentration of NO2 is higherthan SO2, and the overall AQI rating was determined to be moderate. The global experiences clearly demonstratedthat international action had been city specific rather than country oriented, and as a result, statistics show a reductionin PM2.5 of 35%–40% in five years for cities like Beijing and Seoul, whereas cities like Santiago and Mexico Cityhave shown reductions in PM2.5 and PM10 concentration of 73% and 61% in 20 to 25 years, respectively. Recent research published in the Lancet found that over the past five years, CO and Sulphur dioxide levels decreased by 28.2% and 54.1%, respectively, while NO2 and O3 concertation showed no considerable improvement. The August2018 TERI and ARAI report examined several interventions and predicted their possible effects. PM2.5 and PM10 levelsin Delhi and the NCR are above average. A different scenario has been developed in light of the intervention that can improve air quality the most. The NCAP produces an estimated national level target of 20%-30% decreasesin PM2.5 and PM10 concentration by 2024 after taking into consideration the existing international and national studies. This keeps 2018 as the baseline year for concentration comparisons. Gorakhpur City has recently experienced an increase in development activity, which has led to particulate matter.

About Air Quality Assessment using Time Series Analysis Techniques on Air Particles Data

This study investigates the dynamics of air pollution caused by particulate matter (PM10 and PM2.5) in Omsk, Russia. Data collected from the Sensor.Community network between 2021 and 2023 were analyzed using time series analysis methods. The findings reveal a subtle weekly seasonality in the data, potentially attributed to fluctuations in industrial activity and traffic patterns throughout the week. Additionally, a concerning trend of deteriorating air quality was identified, characterized by increasing annual average concentrations of PM10 and PM2.5. The study employed various time series analysis techniques, including Empirical Cumulative Distribution Function (ECDF), Seasonal and Trend decomposition using Loess (STL), and autocorrelation function (ACF) analysis. These methods allowed for the visualization and examination of data distribution, trend identification, and seasonality detection. Furthermore, the Mann-Kendall test confirmed a statistically significant trend of increasing pollution levels over the three-year period. To enhance the accuracy of spatial analysis, data interpolation was performed using kriging with a Gaussian model. This method, compared to simple interpolation, provides a more precise assessment of the spatial distribution of pollutants. The study also implemented Long Short-Term Memory (LSTM) for forecasting hourly pollution values. LSTM exhibited promising results, demonstrating its potential for developing early warning systems, particularly when combined with meteorological data such as wind forecasts. The results highlight the need for effective measures to mitigate air pollution in Omsk and emphasize the importance of utilizing data from diverse sources, including official air quality monitoring stations, to ensure accuracy and reliability in air quality assessments.

ENVIRONMENTAL POLLUTION IN THE CITY OF BUCHAREST

The work covers air quality monitoring tools in Bucharest, major industrial facilities impacting pollution and the city's eight air quality monitoring stations. Data on pollutant emissions from Bucharest's thermal power plants (SO2, NOx and dust) are presented. Finally, the paper graphically presents the annual average concentrations of NO2, SO2, PM10 and O3 at the monitoring stations and examines pollution peaks and daily limit exceedances from 2017 to 2022.

Chemical composition, mutagenicity, and cytotoxicity of urban submicron particulate matter (PM1) in Agra, India

This study, conducted in Agra, India, examined the mass concentrations, chemical compositions, and seasonal variations of submicron particles (PM1). The concentrations of metals, water-soluble inorganic ions (WSIs) including anions (F−, Cl−, NO₃−, SO₄2−) and cations (Ca2+, K+, Mg2+, NH₄+, Na+), organic carbon (OC) and elemental carbon (EC) and polycyclic aromatic hydrocarbons (PAHs) in PM1 extract were determined using Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES), Ion Chromatography, Thermogravimetric Analysis and Gas Chromatography-Mass Spectrometry (GC–MS) respectively. For morphological observation of PM1 particles, Field Emission Scanning Electron Microscopy-Energy Dispersive X-ray spectrometry (FESEM-EDS) was used. The annual average concentration of PM1 was 82.9 ± 33.4 μg/m3, which exceeds the World Health Organisation's (WHO) safe limit for PM2.5 of 5 μg/m3 by a factor of 17. The PM1 mass composition included metals (31 %), WSIs (28 %), OC and EC (9.8 %), and PAHs (0.4 %). Winter recorded the highest PM1 concentration (96.1 ± 25.8 μg/m3), followed by post-monsoon, summer, and monsoon seasons. The average concentration of PAHs was 364.6 ± 226.6 ng/m3. Positive Matrix Factorization (PMF) identified traffic, emissions from biomass/coal and wood combustion, industrial/stationary sources, and secondary aerosols as potential contributors. The Ames test revealed the presence of frameshift mutations and base pair substitutions, especially in winter and post-monsoon. Additionally, PM1 exhibited cytotoxic effects on V-79 cells, with heightened toxicity during winter and prolonged exposure in other seasons. This study underscores the urgent need to address local emission sources and establish regulatory standards for PM1 in urban areas.

Unveiling the elemental composition, sources and health impacts of PM10 over the central Indo-Gangetic plain (IGP) of India

This study investigates the PM10 pollution in the central Indo−Gangetic plain (IGP) of India from February 2018 to December 2019, revealing an annual average PM10 concentration of 193±65 μg m−³. Seasonal concentrations peaked in winter season followed by summer, post-monsoon and monsoon seasons. WD−XRF identifying 35 elements, including major elements like Si, Al, Ca, and Fe, which contributed 18% to PM10 concentrations. The enrichment factor analysis indicates that the Rb, Sr, and Na originate from natural sources, while Fe, Al, Mn, K, Ca, Mg, and Zr have both natural and anthropogenic sources. The remaining elements are primarily of anthropogenic origin. Source apportionment through Principal Component Analysis (PCA) revealed the six key PM10 sources: mixed type (dust+biomass burning, 42%), vehicular (24%), industrial (15%), combustion (7%), agricultural activities (6%), and fossil fuel combustion (6%). Local sources from the northwest (NW) and west (W) directions were dominant, with smaller contributions from trans-boundary regions like Afghanistan and Pakistan. Health risk assessments highlighted non-carcinogenic risks from Mn for adults and children, with additional risks from Al and Cr for children, and carcinogenic risks from Cr for adults. The risk of Al, Cr and Mn in Varanasi are likely driven by combustion related activities, as Cr and Mn in PM10 are commonly associated with industrial and vehicular emission sources.

The Effect of Long-Term Particulate Matter Exposure on Respiratory Mortality: Cohort Study in China.

Particulate matter (PM), which affects respiratory health, has been well documented; however, substantial evidence from large cohorts is still limited, particularly in highly polluted countries and for PM1. Our objective was to examine the potential causal links between long-term exposure to PMs (PM2.5, PM10, and more importantly, PM1) and respiratory mortality. A total of 580,757 participants from the Guangzhou area, China, were recruited from 2009 to 2015 and followed up through 2020. The annual average concentrations of PMs at a 1-km spatial resolution around the residential addresses were estimated using validated spatiotemporal models. The marginal structural Cox model was used to estimate the associations of PM exposure with respiratory mortality, accounting for time-varying PM exposure. Results were stratified by demographics and lifestyle behaviors factors. Among the participants, the mean age was 48.33 (SD 17.55) years, and 275,676 (47.47%) of them were men. During the follow-up period, 7260 deaths occurred due to respiratory diseases. The annual average concentrations of PM1, PM2.5, and PM10 showed a declining trend during the follow-up period. After adjusting for confounders, a 6.6% (95% CI 5.6%-7.6%), 4.2% (95% CI 3.6%-4.7%), and 4.0% (95% CI 3.6%-4.5%) increase in the risk of respiratory mortality was observed following each 1-μg/m3 increase in concentrations of PM1, PM2.5, and PM10, respectively. In addition, older participants, nonsmokers, participants with higher exercise frequency, and those exposed to a lower normalized difference vegetation index tended to be more susceptible to the effects of PMs. Furthermore, participants in the low-exposure group tended to be at a 7.6% and 2.7% greater risk of respiratory mortality following PM1 and PM10 exposure, respectively, compared to the entire cohort. This cohort study provides causal clues of the respiratory impact of long-term ambient PM exposure, indicating that PM reduction efforts may continuously benefit the population's respiratory health.

Development of a photocell-based system supported by simulations for road vehicle counting and particle emissions estimation

This work presents the design and implementation of an innovative system that utilizes photocells for road vehicle counting and computational simulations to estimate traffic-related particulate (PM10) emissions. The integration of photocell technology has resulted in a robust system with reduced counting errors, ensuring high accuracy in correlating traffic counts with emission values. The system was evaluated under various environmental conditions, demonstrating its effectiveness in providing reliable real-time data on the impact of road traffic on particle emissions. The methodology included the use of the ADMS-Urban model for emission estimation and simulations with Ansys Fluent, enabling the acquisition of dependable real-time data. The results indicate a strong agreement between measured and calculated PM10 concentrations, with the system maintaining precision comparable to direct observations. The observed values of 5.0 µg/m3 and 19.2% for absolute and relative errors, respectively, demonstrate the system's remarkable performance, supported by a robust correlation represented by R2 = 0.88. This strong correlation underscores the reliability of the employed model, suggesting its capability to capture and explain a substantial portion of the variability in PM10 concentrations. Validation campaigns conducted on a street in Portugal confirmed the system's ability to accurately count vehicles and estimate particle emissions. The analysis revealed a significant agreement between measured and calculated average PM10 concentrations, with standard deviation values of 7.0 µg/m3 for measured concentrations and 7.5 µg/m3 for calculated concentrations, suggesting consistency between the datasets. These conclusions confirm that the system is a valuable tool for researchers and policymakers, providing crucial insights for the management and improvement of urban air quality, contributing to the development of urban planning policies and environmental sustainability.

Effect of geographical parameters on PM10 pollution in European landscapes: a machine learning algorithm-based analysis

BackgroundPM10, comprising particles with diameters of 10 µm or less, has been identified as a significant environmental pollutant associated with adverse health outcomes in European cities. Understanding the temporal variation of the relationship between PM10 and geographical parameters is crucial for sustainable land use planning and air quality management in European landscapes. This study utilizes Conditional Inference Forest modeling and partial correlation to examine the impact of geographical factors on monthly average concentrations of PM10 in European suburban and urban landscapes during heating and cooling periods. The investigation focuses on two buffer zones (1000 m and 3000 m circle radiuses) surrounding 1216 European air quality monitoring stations.ResultsResults reveal importance and significant correlations between various geographical variables (soil texture, land use, transportation network, and meteorological) and PM10 quality on a continental scale. In suburban landscapes, soil texture, temperature, roads, and rail density play pivotal roles, while meteorological variables, particularly monthly average temperature and wind speed, dominate in urban landscapes. Urban sites exhibit higher R-squared values during both cooling (0.41) and heating periods (0.61) compared to suburban sites (cooling period R-squared: 0.39; heating period: R-squared: 0.51), indicating better predictive performance likely attributed to the less heterogeneous land use patterns surrounding urban PM10 monitoring sites.ConclusionThe study underscores the importance of investigating spatial and temporal dynamics of geographical factors for accurate PM10 air quality prediction models in European urban and suburban landscapes. These findings provide valuable insights for policymakers, urban planners, and environmental scientists, guiding efforts toward sustainable and healthier urban environments.