PM10 Concentrations Research Articles

Sustainable Environmental Management: Evaluating the Effects of the Tail Number Restriction Policy on Air Quality in Lanzhou, China

In recent years, haze pollution in China has garnered significant attention from both the government and the public, with vehicle emissions being identified as a key source of pollution. This study analyzes the impact of the tail number restriction policy (TNRP) on air quality using daily data from Lanzhou, China, in 2020, by employing the regression discontinuity in time (RDiT) method. The results show that the TNRP significantly reduced the air quality index (AQI) and concentrations of PM2.5, PM10, NO2, and CO, indicating a positive effect on air quality, though this is not a long-term standalone solution. Unlike previous policies, the current TNRP restricts traffic during peak hours, yielding consistent effects. This study recommends continuing the restriction policy and developing a comprehensive environmental governance system to promote new energy vehicles and enhance public transportation infrastructure.

The effect of the ultra-low emission zone on PM2.5 concentration in Seoul, South Korea

As studies regarding the positive effect of Low Emission Zones (LEZs) and people's risk perception about air pollution have increased, more powerful and specific traffic regulation policies have been required. London is the first city in the world to implement an Ultra-low Emission Zone (ULEZ) in addition to the existing LEZ. Benchmarking London's ULEZ, a ULEZ policy was implemented in Seoul, South Korea on December 1, 2019. The goal of the policy is to improve air quality by prohibiting entry of vehicles registered nationwide into Seoul's ULEZ that do not meet a specific emission standard including diesel, gasoline, and LPG fuel-based vehicles. This study analyzed the effect of Seoul's ULEZ policy on the five major atmospheric pollutants (PM2.5, PM10, NO2, CO, SO2, O3) concentration in the zone, particularly focusing on PM2.5 concentration. The analysis employs Difference-in-Differences (DD) approach, comparing data from one year before and after the policy's implementation on December 1, 2019. The findings indicate that Seoul's ULEZ policy resulted in a 9.8% increase in PM2.5 concentrations. Conversely, the policy led to reductions in PM10, NO2, CO, and SO2 concentrations by 12.0%, 17.3%, 5.9%, and 10.8%, respectively, while the effect on O3 was statistically insignificant. These empirical results suggest that the ULEZ may need to incorporate more stringent emission standards, expand its coverage, or introduce additional measures to address the unintended increase in PM2.5 concentration.

Impact of COVID-19 restrictions liberalization on air quality: a case study of Chongqing, Southwest China.

To mitigate the societal impact of the COVID-19 pandemic, China implemented long-term restrictive measures. The sudden liberalization at the end of 2022 disrupted residents' daily routines, making it scientifically intriguing to explore its effect on air quality. Taking Chongqing City in Southwest China as an example, we examined the impact of restriction liberalization on air quality, identified potential sources of pollutants, simulated the effects of abrupt anthropogenic control relaxation using a Random Forest Model, and applied an optimized model to predict the post-liberalization pollutant concentrations. The results showed increases in PM2.5 (72.3%), PM10 (67.7%), and NO2 (21.9%) concentrations, while O3 concentration decreased by 20.5%. Although potential pollution source areas contracted, pollution levels intensified with northeastern Sichuan, interior Chongqing, and northern Guizhou being major contributors to pollutant emissions. Anthropogenic emissions accounted for 26.7 ~ 33% changes in PM2.5 and PM10 concentrations while meteorological conditions contributed to 40.2 ~ 43.3% variations observed during the period. The optimized model demonstrated a correlation between predicted and observed values with R2 ranging from 0.70 to 0.89, enabling accurate prediction of post-liberalization pollutant concentrations. This study can enhance our understanding regarding the impact of sudden social lockdown relaxation events on air quality while providing support for urban air pollution prevention.

Air Pollution Reduces the Individuals’ Life Satisfaction Through Health Impairment

Abstract Background The impact of air pollution on individuals’ happiness and life satisfaction (LS), and its relationship to other factors became the focus of recent research. Though, the underlying mechanism of how air pollution impacts LS remains unclear. In this study, we examined the direct and indirect effect of air pollution on individuals’ LS through health mediation. Methods We used longitudinal individual-level data from “Understanding-Society: the UK Household-Longitudinal Study” on 59,492 individuals with 347,377 repeated responses across 11 years (2009-2019) that was linked to yearly concentrations of NO2, SO2, and particulate-matter (PM10, PM2.5) pollution. Generalized structural equation models with multilevel ordered-logistic regression were used to examine the direct effect of air pollution on LS and the indirect effect from health impairment. Results Higher concentrations of NO2 (coefficient = 0.009, 95%CI = 0.007,0.012, p < 0.001), SO2 (coefficient = 0.025, 95%CI = 0.017,0.034, p < 0.001), PM10 (coefficient = 0.019, 95%CI = 0.013, 0.025, p < 0.001), and PM2.5 (coefficient = 0.025, 95%CI = 0.017,0.033, p < 0.001) pollutants were associated with poorer health, while poorer health was associated with reduced LS (coefficient = -0.605, 95%CI = -0.614,-0.595, p < 0.001). Mediation path analysis showed that air pollution impacted individuals’ LS directly and indirectly. The percent of total effect mediated through health was 44.03% for NO2, 73.95% for SO2, 49.88% for PM10, and 45.42% for PM2.5 and the ratio of indirect to direct effect was 0.79 for NO2, 2.84 for SO2, 0.99 for PM10, and 0.83 for PM2.5. Conclusions Health plays a major mediating role in the relationship between air pollution and LS. To alleviate the impact of air pollution on LS, future strategies should focus on health promotion besides reducing air pollution emissions. Key messages • Air pollution is related to lower life satisfaction directly and indirectly through health impairment. • Strategies and policies aiming at reducing air pollution emissions and better health would improve the individual’s life satisfaction and lower the mental health burden.

Light Absorption Properties of Brown Carbon Aerosol During Winter at a Polluted Rural Site in the North China Plain

Brown carbon aerosols (BrC), a subfraction of organic aerosols, significantly influence the atmospheric environment, climate and human health. The North China Plain (NCP) is a hotspot for BrC research in China, yet our understanding of the optical properties of BrC in rural regions is still very limited. In this study, we characterize the chemical components and light absorption of BrC at a rural site during winter in the NCP. The average mass concentration of PM1 is 135.1 ± 82.3 μg/m3; organics and nitrate are the main components of PM1. The absorption coefficient of BrC (babs,BrC) is 53.6 ± 45.7 Mm−1, accounting for 39.5 ± 10.2% of the total light absorption at 370 nm. Diurnal variations reveal that the babs,BrC and organics are lower in the afternoon, attributed to the evolution of planetary boundary layers. BrC is mainly emitted locally, and both the aqueous phase and the photooxidation reactions can increase babs,BrC. Notably, the babs,BrC is reduced when RH > 65%. During foggy conditions, reactions in the aqueous phase facilitate the formation of secondary components and contribute to the bleaching of BrC. This process ultimately causes a decrease in both the absorption Ångström exponent (AAE) and the mass absorption efficiency (MAE). In contrast, the babs,BrC, along with AAE and MAE, rise significantly due to substantial primary emissions. This study enhances our understanding of the light absorption of BrC in rural polluted regions of the NCP.

Extreme Saharan dust events expand northward over the Atlantic and Europe, prompting record-breaking PM10 and PM2.5 episodes

Abstract. Unprecedented extreme Saharan dust (duxt) events have recently expanded northward from subtropical NW Africa to the Atlantic and Europe, with severe impacts on the Canary Islands, mainland Spain and continental Portugal. These six historic duxt episodes occurred on 3–5 and 22–29 February 2020, 15–21 February 2021, 14–17 January 2022, 29 January–1 February 2022, and 14–20 March 2022. We analyzed data of 341 governmental air quality monitoring stations (AQMSs) in Spain (330) and Portugal (11), where PM10 and PM2.5 are measured with European norm (EN) standards, and found that during duxt events PM10 concentrations are underestimated due to technical limitations of some PM10 monitors meaning that they can not properly measure extremely high concentrations. We assessed the consistency of PM10 and PM2.5 data and reconstructed 1690 PM10 (1 h average) data points of 48 and 7 AQMSs in Spain and Portugal, respectively, by using our novel “duxt-r” method. During duxt events, 1 h average PM10 and PM2.5 concentrations were within the range 1000–6000 µg m−3 and 400–1200 µg m−3, respectively. The intense winds leading to massive dust plumes occurred within meteorological dipoles formed by a blocking anticyclone over western Europe and a cutoff low located to the southwest, near the Canary Islands and Cape Verde, or into the Sahara. These cyclones reached this region via two main paths: by deviating southward from the Atlantic mid-latitude westerly circulation or northward from the tropical belt. The analysis of the 2000–2022 PM10 and PM2.5 time series shows that these events have no precedent in this region. The 22–29 February 2020 event led to (24 h average) PM10 and PM2.5 concentrations within the range 600–1840 and 200–404 µg m−3, respectively, being the most intense dust episode ever recorded on the Canary Islands. The 14–20 March 2022 event led to (24 h average) PM10 and PM2.5 values within the range 500–3070 and 100–690 µg m−3 in southeastern Spain, 200–1000 and 60–260 µg m−3 in central Spain, 150–500 and 75–130 µg m−3 in the northern regions of mainland Spain, and within the ranges 200–650 and 30–70 µg m−3 in continental Portugal, respectively, being the most intense dust episode ever recorded in these regions. All duxt events occurred during meteorological anomalies in the Northern Hemisphere characterized by subtropical anticyclones shifting to higher latitudes, anomalous low pressure expanding beyond the tropical belt and amplified mid-latitude Rossby waves. New studies have reported on recent record-breaking PM10 and PM2.5 episodes linked to dipole-induced extreme dust events from North Africa and Asia in a paradoxical context of a multidecadal decrease in dust emissions, a topic that requires further investigation.

Machine learning for air quality index (AQI) forecasting: shallow learning or deep learning?

In this study, several machine learning (ML) models consisting of shallow learning (SL) models (e.g., random forest (RF), K-nearest neighbor (KNN), weighted K-nearest neighbor (WKNN), support vector machine (SVM), artificial neural network (ANN), and deep learning (DL) models (e.g., long short-term memory (LSTM), gated recurrent unit (GRU), recurrent neural network (RNN), and convolutional neural network (CNN)) have been employed for predicting air pollution and its classification. The models were selected based on factors such as prediction accuracy, model generalization, model complexity, and training time. Our study focuses on analyzing and predicting the air quality index (AQI) using daily PM10 concentration as natural pollutants and nine meteorological parameters from March 2013 to February 2022 in Zabol. We also utilized the information gain (IG) method for feature selection. Several measures including accuracy, F1 score, precision, recall, and the area under the curve (AUC), are computed to assess model performance. This study demonstrates the efficacy of DL models, particularly CNN, in predicting the AQI with remarkable accuracy. Our findings reveal that all models effectively classify air quality levels, with an AUC of 0.95 for the good class in both DL and ANN models, significantly outperforming SL models. The AUC values for the hazardous and moderate classes of DL models were also impressive, at 0.90 and 0.83, respectively, underscoring their effectiveness in critical classifications. In terms of performance, CNN achieved an accuracy of 0.60, leading the models, while RF followed closely at 0.58. RNN, GRU, ANN, and SVM each reached an accuracy of 0.57, demonstrating a competitive edge. LSTM and WKNN recorded an accuracy of 0.55, and KNN was slightly lower at 0.53. These results highlight the superior capabilities of DL models in addressing complex air quality classifications, providing invaluable insights for policymakers. By leveraging these advanced techniques, stakeholders can implement more effective strategies to combat air pollution and safeguard public health. It is worth noting that irregular monitoring of air quality data may affect the robustness of our predictions, highlighting the need for more consistent data collection to ensure an accurate representation of pollution levels.

A wearable real-time particulate monitor demonstrates that soaking hay reduces dust exposure.

Affordable particulate matter (PM) monitors suitable for use on horses will facilitate the evaluation of PM mitigation methods and improve the management of equine asthma. Calibrate a real-time wearable PM monitor (Black Beauty [BB]) and compare the PM exposures of horses fed dry or soaked hay. Laboratory calibration; complete cross-over feed trial. Side-by-side sampling with BB monitors and tapered element oscillating microbalances (TEOMs) was performed under varying concentrations of PM from alfalfa hay. Linear regression was used to derive a calibration formula for each unit based on TEOM PM measurements. Precision was evaluated by calculating the coefficient of variation and pairwise correlation coefficients between three BB monitors. PM exposure was measured at the breathing zone of 10 horses for 8 h after they were fed dry or soaked hay. Repeated measures generalised linear models were constructed to determine the effect of hay treatment and measurement duration (initial 20-min vs. 8-h) upon exposure to PM with diameters smaller than or equal to 10 μm (PM10) and 2.5 μm (PM2.5). BB monitor PM2.5 and PM10 measurements were linearly correlated with TEOM data (coefficient of determination r2 > 0.85 and r2 > 0.90 respectively), but underestimated PM2.5 mass concentrations by a factor of 4 and PM10 concentrations by a factor of 44. Measures from the three BB monitors had a coefficient of variation <15% and pairwise r > 0.98. Feeding soaked hay significantly reduced average PM2.5 exposures (20-min: dry: 160 μg/m3, soaked: 53 μg/m3, p < 0.0001; 8-h: dry: 76 μg/m3, soaked: 31 μg/m3, p = 0.0008) and PM10 exposures (20-min: dry: 2829 μg/m3, soaked: 970 μg/m3, p < 0.0001; 8-h: dry: 1581 μg/m3, soaked: 488 μg/m3, p = 0.008). No health outcome measures were collected. With appropriate corrections, the BB monitor can be used to estimate horse PM exposure. While 20-min measurements yielded higher estimates of exposure than 8-h measurements, both intervals demonstrate that soaking hay reduces PM exposures by more than 50%.

Air quality in different urban hotspots in a metropolitan city in India and the environmental implication.

This research study investigates hourly data on concentrations of five major air pollutants such as particulate matter (PM10, PM2.5) and gaseous pollutants (SO2, NO2, CO) measured during 2022 at four hotspot sites (industrial site, traffic site, commercial site, harbour, and one residential site) in Chennai, India. The analysis encompasses temporal variations spanning annual, seasonal, and diurnal variations in the pollutants. Notably, PM10 and CO emerge as the predominant pollutants, with the highest concentrations at industrial and traffic sites (PM10: 67.64 ± 40.77µg/m3, CO: 1.41 ± 0.84mg/m3; traffic site: PM10: 58.67 ± 20.05µg/m3, CO: 0.99 ± 0.57mg/m3). Seasonal dynamics reveal prominent winter spikes in particulate matter (PM10, PM2.5) and carbon monoxide (CO) concentrations, while nitrogen dioxide (NO2) and sulphur dioxide (SO2) levels peak during the summer season, particularly in the harbour area. The proximity to roadways exerts a discernible influence on diurnal patterns, with traffic sites showcasing broader rush hour peaks compared to sharper spikes observed at other sites. Furthermore, distinct bimodal patterns are evident for PM10 and PM2.5 concentrations in residential and harbour areas. A common lognormal distribution pattern is identified across the studied sites, suggesting consistent air quality trends despite contrasting locations. The conditional probability function (CPF) is used in conjunction with local meteorological conditions for identifying key pollution sources in each location. The implementation of polar plots emphasizes industries as principal local sources of pollution, at industrial sites significantly contributing to PM10, SO2, and NO2 concentrations under specific wind conditions. The main objective of the present study is to facilitate a good understanding of pollutant dynamics, pollution sources, and their intricate interplay with meteorological factors, thereby contributing to the formulation and implementation of effective air pollution control and mitigation strategies.

The Formaldehyde, TVOCs, PM2.5 and PM10 Concentrations at Outdoor Street Food in Kalasin, Thailand

Human health and well-being are directly impacted by air quality. Cardiovascular and respiratory problems have been linked to exposure to pollutants such as formaldehyde, total volatile organic compounds (TVOCs), and particulate matter 2.5 and 10 micrometers (PM2.5 and PM10). This study aimed to quantify formaldehyde, TVOCs, PM2.5, and PM10 levels in the outdoor air at street food vendors in Kalasin, Thailand. The study was conducted at street food vendors in the province of Kalasin. Formaldehyde values at the top five food stalls in Kalasin ranged from 0.58 to 1.41 mg/m3. The PM2.5 values ranged from 2.29 to 9.43 ug/m3, and the TVOC values ranged from 2.91 to 7.02 ug/m3, with PM10 concentrations ranging from 2.86 to 7.29 ug/m3. Drawing on data about these pollutant concentrations across five distinct locations, the following recommendations can be put forth: Elevated levels of formaldehyde, TVOCs, PM2.5, and PM10 in certain areas can have detrimental effects on the environment and human well-being.

Vertical distribution of ambient air pollutants (PM2.5, PM10, NOX, and NO2); A systematic review

BackgroundNumerous investigations have explored variations in vertical air pollutant concentrations, yielding diverse findings. So, we have conducted this systematic review to gain a more comprehensive understanding of the vertical distribution of air pollutant concentrations (PM2.5, PM10, NOX, and NO2) and the influencing factors. MethodsRelevant studies were identified by searching the three central electronic databases, PubMed, Web of Science, and Scopus, from the beginning of 2000 to the end of 2023. This study included original articles published in English that examine the pollutant concentration variations below 500 m. FindingOf 3614 articles, 57 studies met our criteria. Our findings showed a decline in PM2.5 and NOX concentrations as altitude increased, while NO2 concentrations exhibited an increase. Conversely, no statistically significant relationship was identified between altitude and PM10 concentrations. The regression analysis yielded coefficients for the relationship between concentration and altitude (0–500 m) as follows: PM2.5 (−0.11), PM2.5/PM10 (0.0008), NOX (−0.11), and NO2 (0.13). Conducting additional research on this topic and investigating the impact of meteorological parameters on pollutant concentrations at different altitudes enhances our understanding of the vertical distribution of pollutant concentrations.

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.

Quantifying the noncarcinogenic and carcinogenic risks resulting from the inhalation of PM2.5-bound metals: A multicity analysis and implications for public health

Toxic metals in PM2.5 represent a growing public health concern because of their potential health effects. Ambient PM2.5 samples were collected from five provinces around Bangkok from December 2020 to December 2021. The concentrations of PM2.5 and metals (arsenic, cadmium, chromium, cobalt, nickel, lead, and vanadium) were analyzed. The noncarcinogenic and carcinogenic risks were evaluated via Monte Carlo simulations in four age groups (0 to <6, 6 to <12, 12 to <18, 18–70 years), with a dataset comprising 2282 heavy metal assays within the particulate matter. Significantly higher PM2.5 concentrations were detected in provinces with higher factory densities, as well as higher concentrations during haze periods than during non-haze periods. The HI values for all age groups and provinces were lower than 1, indicating acceptable noncarcinogenic risks. The carcinogenic risks from PM2.5-bound metals varied between 7.08×10−7 and 4.29×10−6, indicating increased cancer risks, for preschool children and adults. Cancer risk was highest in industrial areas, followed by areas with dense traffic, and lowest in agricultural areas. The results indicated that Thailand's current air quality regulations, which primarily provide only lead limits, are insufficient considering the high carcinogenic potential of other metals. The Monte Carlo simulation results confirmed that lowering Thailand’s 24-h PM2.5 standard from 37.5 to either 25 or 15 µg m−3, as specified in the 2021 World Health Organization guidelines, would decrease cancer risk by 9.63–43.18 %.

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.

A study on indoor air quality at daycare centers using IoT environmental sensors

ABSTRACT Indoor air quality (IAQ) is a major concern at daycare centers, due to the weaker immune system of children. Although IAQ is typically measured in accordance with the ISO-16000 standard, environmental sensors have recently become widely used due to their ability to consistently monitor air quality at a lower cost. In this study, We installed IoT sensors indoors and outdoors at ten daycare centers to measure PM(PM2.5, PM10), CO2, temperature, and humidity, as well as window opening-closing sensors to identify ventilation behavior. Indoor PM concentrations were consistently lower than outdoor levels, increasing from 7:00 AM, peaking between 9:00 AM and 12:00 PM, and decreasing during nap time (12:00 PM to 2:00 PM). CO2 levels were generally higher indoors than outdoors, peaking at 2:00 PM before decreasing. Windows were opened more frequently at 7:00 AM, 8:30 AM, and 3:00 PM. The correlation analysis revealed that significant factors influencing CO2 concentration were window opening duration. Ventilation rates per person at the daycare centers ranged from 1.77 to 10.69 m3/h, below both Korean and ASHRAE guidelines. The results of this study can provide valuable insights for developing strategies to improve indoor air quality in daycare centers that depend on window ventilation.

ЭМУЛЯЦИЯ ПОКАЗАНИЙ ДАТЧИКОВ КАЧЕСТВА ВОЗДУХА В ГОРОДСКОЙ СРЕДЕ УМНОГО ГОРОДА

Urban air pollution poses a serious threat to human health. To monitor it, both individual sensors and systems are used to assess the concentration of dust particles PM1, PM2.5, PM10 and organic compounds. However, the reliability of the sensor system cannot be 100 percent. From time to time, certain sensors in a distributed system fail. For this reason, it is very useful to emulate their readings based on the readings of the remaining sensors. The work describes a data set and proposes a machine learning model, which, based on the readings of functional sensors and weather conditions at the data collection sites, simulates the readings of a failed sensor. The accuracy of such emulation for certain types of pollution has been assessed (the coefficient of determination ranges from 0.43 to 0.61).

Интегрирование химической транспортной модели и искусственной нейронной сети для прогноза концентрации PM10

A hybrid model for forecasting PM10 surface concentrations in the Moscow region, consisting of a chemical transport model (CTM) and an artificial neural network (ANN), with the use of PM10 automatic measurements data was developed and tested. The ANN was trained to predict PM10 concentrations based on the forecasts of CTM concentrations and meteorological parameters on a 2 km grid. The results of testing the ANN based on independent samples, including the episodes of high PM10 pollution, are discussed.

Assessment of indoor air pollution in causing respiratory illness among women residing in rural areas of Mysuru

Introduction: The use of inefficient and harmful fuels and technology within and outside the home is the primary cause of indoor air pollution. Approximately 2.4 billion people on the planet still cook over open flames and inefficient stoves using solid fuels and kerosene. The availability of cleaner cooking options varies more in rural than in metropolitan settings. Therefore, there is a need to determine the prevalence of indoor air pollution among rural women, its association with respiratory disorders, and the frequency of respiratory illnesses among rural women residing in houses with or without indoor air pollution. Materials and methods: This analytical cross-sectional study was done for a period of six months from June to November 2020. The study conducted in 210 households living in the rural field practice areas of JSS Medical College, Mysuru. For the selection of villages, a multistage random sampling technique was used. Three villages are selected from the rural setting: Suttur, Hadinaru, and Kadakola. The households were included by probability proportionate to the size sampling technique. Pre-tested, semi-structured, phase and content validated questionnaires were used to obtain the details on socio-demographic characteristics, meter values of indoor air pollutants like Particulate Matters (PM1, PM2.5, PM10) and Volatile Organic Compounds (VOCs), respectively with the factors affecting respiratory illness and the episodes of the illness. Data was entered in Microsoft Excel and analyzed using descriptive and inferential statistics using SPSS V.26. Results: The prevalence of indoor air pollution in the present study was 13.3%. In the homes of rural women, there was a statistically significant variation in the concentration of the three particulate matter sources that cause indoor air pollution. Additionally, among the women living in rural families, there was a statistically significant difference in the concentration of PM10 that caused respiratory disease. Conclusion: The current study demonstrated the higher concentrations of pollutants such as PM10, PM2.5, and PM1.0, as well as their significant relation with indoor air pollution in rural families. To prevent indoor air pollution and its health risks, clean energy must be used for household needs.

Air quality and wheeze symptoms in a rural children's cohort near a drying saline lake

BackgroundIn California, climate change and competing water demands are intensifying the desiccation of the Salton Sea, a large land-locked “sea” situated near the southeastern rural US-Mexico border region known as the Imperial Valley. MethodsTo examine the possible effects of living near a saline lake on children's respiratory health, we analyzed the relationship between respiratory health symptoms and ambient PM concentrations among a predominantly Latino/Hispanic cohort of 722 school age children. Guardians completed a survey of their child's wheeze and respiratory health symptoms over the past 12 months, adapted from the International Study of Asthma and Allergies in Childhood (ISAAC). Exposure to dust storm hours (hourly concentrations >150 μg/m3 for PM10) was estimated using a network of regulatory monitors. ResultsBetween 2017 and 2019, children were exposed to 98 to 395 dust event hours annually. We observed disparate effects for dust events and wheeze among children living near the Salton Sea. Every additional 100 dust storm hours per year among children living near the Sea (<11 km) was associated with a 9.5 percentage point increase in wheeze (95% CI: 3.5, 15.4), a 4.6 percentage point increase in bronchitic symptoms (95% CI: 0.18, 10.2) and a 6.7 percentage point increase in sleep disturbance due to wheeze (95% CI: 0.96, 12.4). Similarly, increases in PM10 were also associated with greater reported wheeze and bronchitic symptoms among those living near the Sea, compared to children living ≥11 km from the Sea. There was no association of dust storms or PM10 with wheeze or bronchitic symptoms among the children residing farther from the Sea. ConclusionWe observed stronger adverse impacts of PM10 and dust events on respiratory health among those living closer to the drying Salton Sea, compared to children living farther away. In this community of predominantly low-income residents of color, these impacts raise environmental justice concerns about the effects of the drying Salton Sea on public health.

Causes of the unremitting high ambient levels of PM10 in a suburban background site in NE Spain

Atmospheric PM10 and benzo(a)pyrene (BaP) concentrations in Manlleu (NE Spain) have remained high from 2008 to 2023, frequently exceeding EU limit/target values, and reaching BaP levels up to six times higher than urban averages in Spain. Furthermore, PM speciation campaigns were carried out in 2013, 2014–2015, 2016–2017 and 2021–2022. Chemical mass closure for autumn-winter showed a consistent PM10 composition for the different PM speciation campaigns, comprising 46–53% organic matter (OM), 18–26% secondary inorganic aerosol (SIA), 13–23% mineral matter (MM), and 5–9% elemental carbon (EC). Trend analysis revealed very light decrease and constant concentrations for PM10 and BaP, respectively over the study period, emphasizing the need for compliance with current and forthcoming EU air quality directives, the last aiming to halve PM10 limit values. Source apportionment of samples of the sporadic campaigns identified biomass burning (BB, 17.5 μg m−3, 48%) and MM and industry (16.3 μg m−3, 44%) as the main autumn-winter PM10 contributors, with high SIA concentrations attributed to several factors, including high ammonia (NH3) emissions. Local topography and meteorological conditions contribute to aggravate PM10 pollution. While metal concentrations have decreased since 2013, suggesting reduced industrial emissions, persistently high OM and EC concentrations indicate ongoing issues with BB emissions from domestic, commercial, and agricultural sources. Online analysis of black carbon (BC) and non-refractory PM1 components during winter 2016–2017 confirmed domestic and commercial BB as the primary sources of the BB contributions. These findings highlight the need of the implementation of more effective measures in reducing BB and agricultural/farming NH3 emissions. This study highlights the relevance of these issues for similar towns, the probable unremitting problem over the last decade, and the necessity of enhanced monitoring in small cities and policy actions to meet air quality standards under the new EU directive.