Levels Of Particulate Matter Research Articles

Comparative analysis of waste-derived pyrolytic fuels applied in a contemporary compression ignition engine

This article presents the outcomes of research regarding pyrolysis oils obtained from waste sources (WPO) used to power a compression-ignition engine. Oils obtained in an industrial process based on polypropylene (PPO), polystyrene (PSO) and used car tires (TPO) were used. Prior to conducting engine tests, a in-depth examination of the tested fuels parameters was undertaken. For the tests was used an advanced single-cylinder research engine utilizing split fuel injection technique. Emission analysis was performed using multi-compound FTIR analytical system. The WPO were blended with diesel fuel in proportions of 20%, on the mass basis and tested at middle engine load and variable EGR rates. Tests have shown that modern combustion systems compliant with the Tier 4 standard with multi-pulse injection can handle fuels with a WPO content of 20% without the need for recalibration. The addition of PPO did not significantly affect the emission, while mixing with PSO resulted in elevated levels of hydrocarbon and carbon monoxide emissions. Regarding to the mixture with TPO, increased levels of particulate matter, sulfur oxides, aromatic compounds and formic acid were observed.

Seasonal and annual emission trends of PM2.5 and PM10 over the national capital Delhi from 2015 to 2020

Introduction: Many urban cities in India, including National Capital Territory (NCT) of Delhi, have been adversely affected by air pollution since 2010s. Factors like atmospheric circulation, topography, local weather patterns, vehicular emission, industrial activities, waste burning etc. lead to extreme air pollution which is a growing threat to public health. Materials and methods: The study was conducted to examine severity of air quality in Delhi from 2015 to 2020. For this purpose, Particulate Matters (PM2.5 and PM10) measurements were done at ITO, Mandir Marg, Punjabi Bagh, Anand Vihar, and R K Puram for a period of 2015 to 2020. Everyday data was collected to interpret and analyze the trend of air pollution both seasonally and annually. Results: There was a significant increase in particulate matter levels at ITO, Mandir Marg, and RK Puram in the year 2016 while in the case of Anand Vihar the highest levels were measured in 2017. Also, Punjabi Bagh showed the highest PM10 levels in 2017. The annual average data for all five sites indicates that PM2.5 and PM10 levels were above the air quality standard throughout the study period. Anand Vihar showed the highest level of particulate matter among all five locations during the study period of 2015 to 2020. The change in seasons is also one of the important factors affecting air quality. Overall, PM2.5 and PM10 concentration were recorded maximum during winters i.e. 218.07 μg/m3 and 358.80 μg/m3 respectively. Conclusion: The results conclude that air quality deteriorated more during winters>autumn>summers>monsoon. Also, the air quality index (AQI) recorded for Delhi during the study period was highest in 2016, respectively. Though AQI decreased over the years, it lied in poor quality only

Longitudinal Analysis of Annual PM2.5 Concentration Variations in Blantyre City, Malawi

This study presents current levels of fine particulate matter, with aerodynamic diameter of less than 2.5 micrometre (PM2.5) in the city of Blantyre, Malawi measured between June 2021 to May 2022. PM2.5 measurements were done in 18 different locations (spanning greater than 2 km apart) using Dylos DC1100 PRO Laser Particle Counter (2018 model). The sampling points were; 3 school campuses, 3 hospitals, 3 industrial areas, 3 open markets, 3 residential areas, and 3 commercial/ business centres (CBC) of Blantyre. PM2.5 monitoring was conducted between 10:00-12:00 hours and 15:00-17:00 hours local time. The results showed that the hourly mean PM2.5 concentrations (µg/m3) for the 10:00-12:00 time period were; 43 ± 23 µg/m3 for school campuses, 35 ± 16 µg/m3 for hospitals, 62 ± 38 µg/m3 for industrial areas, 44 ± 26 µg/m3 for markets, 40 ± 21 µg/m3 for residential areas and 35 ± 16 µg/m3 for CBC. The results showed that the hourly mean PM2.5 concentrations (µg/m3) for the 15:00-17:00 time period were; 38 ± 22 µg/m3 for school campuses, 34 ± 18 µg/m3 for hospitals, 57 ± 37 µg/m3 for industrial areas, 42 ± 25 µg/m3 for markets, 36 ± 23 µg/m3 for residential areas and 34 ± 18 µg/m3 for CBC. Significant increases of PM2.5 levels were observed in school campuses, residential areas and CBC during the months of June-October, which are windy and drier. On the other hand, lowest concentration of PM2.5 was observed during the warm season (November-March) across the sampling locations over Blantyre. Based on these findings, this study recommends further investigation of long-term concentration of PM2.5 in Blantyre city because it is hazardous and likely to cause health implications to the local population. Furthermore, interventions should be sought to reduce PM2.5 concentration in the city.

Dissecting heritability, environmental risk, and air pollution causal effects using > 50 million individuals in MarketScan

Large national-level electronic health record (EHR) datasets offer new opportunities for disentangling the role of genes and environment through deep phenotype information and approximate pedigree structures. Here we use the approximate geographical locations of patients as a proxy for spatially correlated community-level environmental risk factors. We develop a spatial mixed linear effect (SMILE) model that incorporates both genetics and environmental contribution. We extract EHR and geographical locations from 257,620 nuclear families and compile 1083 disease outcome measurements from the MarketScan dataset. We augment the EHR with publicly available environmental data, including levels of particulate matter 2.5 (PM2.5), nitrogen dioxide (NO2), climate, and sociodemographic data. We refine the estimates of genetic heritability and quantify community-level environmental contributions. We also use wind speed and direction as instrumental variables to assess the causal effects of air pollution. In total, we find PM2.5 or NO2 have statistically significant causal effects on 135 diseases, including respiratory, musculoskeletal, digestive, metabolic, and sleep disorders, where PM2.5 and NO2 tend to affect biologically distinct disease categories. These analyses showcase several robust strategies for jointly modeling genetic and environmental effects on disease risk using large EHR datasets and will benefit upcoming biobank studies in the era of precision medicine.

Particulate Matter Exposure during Pregnancy and Childhood Leukemia Incidence

Leukemia is the most common childhood cancer and its etiology could be related to various environmental contaminants such as particulate matter (PM). The objective of our study is to evaluate the potential association between exposure to PM during pregnancy and the incidence of childhood leukemia. We established a population-based nationwide cohort using the Spanish Birth Registry Statistics database of the National Statistics Institute. We used spatiotemporal land use random forest models to estimate the concentrations of PM10 and PM2.5 for the entire pregnancy and by trimesters. We conducted logistic regression analyses adjusted for various covariates. In addition, we fitted generalized additive models (GAMs) to estimate the non-linear relationship between PM levels and leukemia incidence. The study included 3,112,123 children and 1066 cases of leukemia. The results for the continuous variable of PM10 exposure levels suggested an increased risk of childhood leukemia to be associated with higher exposure. The results for the categorized PM10 variable suggest an increased risk of childhood leukemia among pregnant women whose exposure levels were higher than the median (third and fourth quartiles). The results for PM2.5 were weaker. We found association between exposure to PM10 during pregnancy and an increased risk of childhood leukemia. Our findings indicate that public health interventions should aim to reduce air pollution to lower the incidence of childhood leukemia.

Assessing the risks associated with indoor and outdoor air quality in relation to the geographic placement of nursing home

Indoor air quality significantly impacts the well-being and health of elderly residents in nursing homes. This study was conducted to explore the connection between indoor and outdoor PM (Particulate Matter) concentrations in nursing homes and their association with the facilities' location and construction characteristics. The findings revealed that indoor PM2.5 and PM10 concentrations ranged from 0.2 to 124 μg/m3 and 2–188.4 μg/m3, respectively, which were approximately 12.67 and 1.25 times higher than their outdoor counterparts. A strong correlation (P < 0.05) was identified between indoor PM levels and various factors, including proximity to parks, passenger terminals, and gas stations, as well as building attributes such as single-glazed windows, ceramic floor coverings, and the use of radiators. The risk assessment indicated that carcinogenic risk factors were well within acceptable limits for all nursing homes. However, it's important to note that certain PM components, particularly polycyclic aromatic hydrocarbons (PAH), may have long-term adverse effects on the health of nursing home residents. Even though indoor PM levels met the standards established by the U.S. Environmental Protection Agency (USEPA) for particulate matter risk assessments, the study emphasized that even low levels of indoor air pollutants can affect the health and well-being of older adults, particularly considering the increased vulnerability associated with aging. Consequently, the study underscores the importance of nursing home location selection and the regular monitoring of particulate matter concentrations. These measures are essential for enhancing air quality within nursing homes, ultimately contributing to the improved well-being and health of their residents.

Lung deposition of PM-bound trace elements and corresponding human health risk assessment in commercial kitchens serving Mediterranean cuisine

The service offered by commercial kitchens and the catering industry is becoming more and more popular all around the world. However, the health risks of sector workers are often overlooked. In this study, size-segregated Particulate Matter (PM) samples were collected from the indoor environments of five commercial kitchens, and elemental concentrations were quantified to estimate the corresponding health risks. Lung-deposited fractions of trace elements in three different regions of the respiratory tract were estimated and used to assess carcinogenic and non-carcinogenic health risks. Besides, the deterioration in lung capacity of the cooks was also evaluated by spirometry tests to correlate indoor pollutants. Exposure to trace elements was dominated by Na, S, Al, Fe, and Mg. Estimated carcinogenic risk through inhalation exposure to Cr was between the moderate risk level of 10−6 to 10−4, while the Hazard Index (HI) values were lower than the acceptable level of 1. This, along with elevated indoor PM levels, represented a significant potential for carcinogenic health hazards for the kitchen staff. According to the results of the lung function tests, a significant decrease was determined in the respiratory functions of the cooks, and it was associated with elevated exposure to fine PM, Pb, Cr, Cu, K, Ni, and Mn. Workplace health and safety measures to better protect the health of kitchen staff are urgently needed and should attract more public attention.

Enhancing Dust Control for Cage-Free Hens with Electrostatic Particle Charging Systems at Varying Installation Heights and Operation Durations

The poultry industry is shifting towards more sustainable and ethical practices, including adopting cage-free (CF) housing to enhance hen behavior and welfare. However, ensuring optimal indoor air quality, particularly concerning particulate matter (PM), remains challenging in CF environments. This study explores the effectiveness of electrostatic particle ionization (EPI) technology in mitigating PM in CF hen houses while considering the height at which the technology is placed and the duration of the electric supply. The primary objectives are to analyze the impact of EPI in reducing PM and investigate its power consumption correlation with electric supply duration. The study was conducted in a laying hen facility with four identical rooms housing 720 laying hens. The study utilized a Latin Square Design method in two experiments to assess the impact of EPI height and electric supply durations on PM levels and electricity consumption. Experiment 1 tested four EPI heights: H1 (1.5 m or 5 ft), H2 (1.8 m or 6 ft), H3 (2.1 m or 7 ft), and H4 (2.4 m or 8 ft). Experiment 2 examined four electric supply durations: D1 (control), D2 (8 h), D3 (16 h), and D4 (24 h), through 32 feet corona pipes. Particulate matter levels were measured at three different locations within the rooms for a month, and statistical analysis was conducted using ANOVA with a significance level of ≤0.05. The study found no significant differences in PM concentrations among different EPI heights (p &gt; 0.05). However, the duration of EPI system operation had significant effects on PM1, PM2.5, and PM4 concentrations (p &lt; 0.05). Longer EPI durations resulted in more substantial reductions: D2—17.8% for PM1, 11.0% for PM2.5, 23.1% for PM4, 23.7% for PM10, and 22.7% for TSP; D3—37.6% for PM1, 30.4% for PM2.5, 39.7% for PM4, 40.2% for PM10, and 41.1% for TSP; D4—36.6% for PM1, 24.9% for PM2.5, 38.6% for PM4, 36.3% for PM10, and 37.9% for TSP compared to the D1. These findings highlight the importance of prolonged EPI system operation for enhancing PM reduction in CF hen houses. However, utilizing 16 h EPI systems during daylight may offer a more energy-efficient approach while maintaining effective PM reduction. Further research is needed to optimize PM reduction strategies, considering factors like animal activities, to improve air quality and environmental protection in CF hen houses.

Differential Inflammatory Cytokine Elaboration in Serum from Brick Kiln Workers in Bhaktapur, Nepal.

Previous studies involving workers at brick kilns in the Kathmandu Valley of Nepal have investigated chronic exposure to hazardous levels of fine particulate matter (PM2.5) common in ambient and occupational environments. Such exposures are known to cause and/or exacerbate chronic respiratory diseases, including chronic obstructive pulmonary disease (COPD) and asthma. However, there is a paucity of data regarding the status of systemic inflammation observed in exposed workers at brick manufacturing facilities within the country. In the current study, we sought to elucidate systemic inflammatory responses by quantifying the molecular cytokine/chemokine profiles in serum from the study participants. A sample of participants were screened from a kiln in Bhaktapur, Nepal (n = 32; 53% female; mean ± standard deviation: 28.42 ± 11.47 years old) and grouped according to job category. Blood was procured from participants on-site, allowed to clot at room temperature, and centrifuged to obtain total serum. A human cytokine antibody array was used to screen the inflammatory mediators in serum samples from each of the participants. For the current study, four job categories were evaluated with n = 8 for each. Comparisons were generated between a control group of administration workers vs. fire master workers, administration workers vs. green brick hand molders, and administration workers vs. top loaders. We discovered significantly increased concentrations of eotaxin-1, eotaxin-2, GCSF, GM-CSF, IFN-γ, IL-1α, IL-1β, IL-6, IL-8, TGF-β1, TNF-α, and TIMP-2 in serum samples from fire master workers vs. administration workers (p < 0.05). Each of these molecules was also significantly elevated in serum from green brick hand molders compared to administration workers (p < 0.05). Further, each molecule in the inflammatory screening with the exception of TIMP-2 was significantly elevated in serum from top loaders compared to administration workers (p < 0.05). With few exceptions, the fire master workers expressed significantly more systemic inflammatory molecular abundance when compared to all other job categories. These results reveal an association between pulmonary exposure to PM2.5 and systemic inflammatory responses likely mediated by cytokine/chemokine elaboration. The additional characterization of a broader array of inflammatory molecules may provide valuable insight into the susceptibility to lung diseases among this population.

IoT-based monitoring system and air quality prediction using machine learning for a healthy environment in Cameroon.

This paper is aimed at developing an air quality monitoring system using machine learning (ML), Internet of Things (IoT), and other elements to predict the level of particulate matter and gases in the air based on the air quality index (AQI). It is an air quality assessor and therefore a means of achieving the Sustainable Development Goals (SDGs), in particular, SDG 3.9 (substantial reduction of the health impacts of hazardous substances) and SDG 11.6 (reduction of negative impacts on cities and populations). AQI quantifies and informs the public about air pollutants and their adverse effects on public health. The proposed air quality monitoring device is low-cost and operates in real-time. It consists of a hardware unit that detects various pollutants to assess air quality as well as other airborne particles such as carbon dioxide (CO2), methane (CH4), volatile organic compounds (VOCs), nitrogen dioxide (NO2), carbon monoxide (CO), and particulate matter with an aerodynamic diameter of 2.5 microns or less (PM2.5). To predict air quality, the device was deployed from November 1, 2022, to February 4, 2023, in certain bauxite-rich areas of Adamawa and certain volcanic sites in western Cameroon. Therefore, machine learning algorithm models, namely, multiple linear regression (MLR), support vector regression (SVR), random forest regression (RFR), XGBoost (XGB), and K-nearest neighbors (KNN) were applied to analyze the collected concentrations and predict the future state of air quality. The performance of these models was evaluated using mean absolute error (MAE), coefficient of determination (R-square), and root mean square error (RMSE). The obtained data in this study show that these pollutants are present in selected localities albeit to different extents. Moreover, the AQI values obtained range from 10 to 530, with a mean of 132.380 ± 63.705, corresponding to moderate air quality state but may induce an adverse effect on sensitive members of the population. This study revealed that XGB regression performed better in air quality forecasting with the highest R-squared (test score of 0.9991 and train score of 0.9999) and lowest RMSE (test score of 1.5748 and train score of 0. 0073) and MAE (test score of 0.0872 and train score of 0.0020), while the KNN model had the worst prediction (lowest R-squared and highest RMSE and MAE). This embryonic work is a prototype for projects in Cameroon as measurements are underway for a national spread over a longer period of time.

Characterization of Wildland Firefighters' Exposure to Coarse, Fine, and Ultrafine Particles; Polycyclic Aromatic Hydrocarbons; and Metal(loid)s, and Estimation of Associated Health Risks.

Firefighters' occupational activity causes cancer, and the characterization of exposure during firefighting activities remains limited. This work characterizes, for the first time, firefighters' exposure to (coarse/fine/ultrafine) particulate matter (PM) bound polycyclic aromatic hydrocarbons (PAHs) and metal(loid)s during prescribed fires, Fire 1 and Fire 2 (210 min). An impactor collected 14 PM fractions, the PM levels were determined by gravimetry, and the PM-bound PAHs and metal(loid)s were determined by chromatographic and spectroscopic methodologies, respectively. Firefighters were exposed to a total PM level of 1408.3 and 342.5 µg/m3 in Fire 1 and Fire 2, respectively; fine/ultrafine PM represented more than 90% of total PM. Total PM-bound PAHs (3260.2 ng/m3 in Fire 1; 412.1 ng/m3 in Fire 2) and metal(loid)s (660.8 ng/m3 versus 262.2 ng/m3), distributed between fine/ultrafine PM, contained 4.57-24.5% and 11.7-12.6% of (possible/probable) carcinogenic PAHs and metal(loid)s, respectively. Firefighters' exposure to PM, PAHs, and metal(loid)s were below available occupational limits. The estimated carcinogenic risks associated with the inhalation of PM-bound PAHs (3.78 × 10-9 - 1.74 × 10-6) and metal(loid)s (1.50 × 10-2 - 2.37 × 10-2) were, respectively, below and 150-237 times higher than the acceptable risk level defined by the USEPA during 210 min of firefighting activity and assuming a 40-year career as a firefighter. Additional studies need to (1) explore exposure to (coarse/fine/ultrafine) PM, (2) assess health risks, (3) identify intervention needs, and (4) support regulatory agencies recommending mitigation procedures to reduce the impact of fire effluents on firefighters.

Airborne particle radioactivity during desert dust days in Cyprus

Mediterranean countries are often affected by desert dust storms, which have significant effects on the environment and public health. We compared airborne particle radioactivity levels during desert dust and non-dust days in Cyprus.Gross α- and β-radioactivity from Total Suspended Particle (TSP) samples, collected at two urban routine monitoring stations in Limassol and Nicosia, were available for the period 2017–2020 and 2008–2020, respectively. Radionuclides 137Cs and 40K, from TSP samples, were also available from a semi-industrial monitoring station in Nicosia during 2008–2020. Information on desert dust presence, dust origin, particulate matter (PM) levels, and solar activity (KP index and solar sunspot numbers - SSN) were also obtained. We used linear regression models adjusting for seasonality and long-term trends, and solar activity to assess the effect of dust storms on TSP gross α- and β-, and 137Cs and 40K radioactivity levels.Gross α- and β-radioactivity, and 137Cs and 40K radioactivity levels were significantly higher on days with desert dust compared to days characterized with no influence of desert dust. Levels of gross α- and β-radioactivity during dust days were higher when dust originated from the Middle East deserts than from the Sahara Desert. The same trend was observed for the ratios 137Cs to 40K and 137Cs to PM10. Conversely, ratios of TSP gross α- and β-radioactivity to PM10 were significantly lower during desert dust days in comparison to days without dust influence.This study suggests that desert dust increase both TSP gross α- and β-radioactivity, as well as 137Cs and 40K radioactivity levels. Further studies should clarify the contribution of anthropogenic and other natural sources to the emission or transportation of particles radioactivity, to better mitigate future exposures.

Chemical characterization of submicron particulate matter (PM1) and its source apportionment using positive matrix factorization

AbstractThe present study was conducted to address four key questions: (i) What are the levels of submicron particulate matter at the study area?, (ii) which are the major contributing sources of these particles?, and (iii) is there any seasonal changes in the levels of pollutants at the study site? Thus, the study was conducted at an urban residential site of Jaipur City, India, to determine the elemental and ionic composition of toxic elements associated with PM1 using inductively coupled plasma optical emission spectroscopy and ion chromatography to reveal specific sources. Monitoring was done for a period of 8 months between October 2020 and May 2021 considering three seasons: winter (December–February), pre‐monsoon (March–May), and post‐monsoon (October–November). PM1 samples were found to be highly enriched with Ag, Cd, B, Ni, and Zn. PM1 mass concentrations were observed to be greater in winter (104.13 ± 30.16 µg m−3) and lower in the pre‐monsoon season (83.62 ± 19.40 µg m−3). Ion concentrations (Cl−, NO32−, and SO42−) followed a similar pattern to PM1 concentrations. Source apportionment by positive matrix factorization at the study site revealed six major sources of pollutants (soil dust, agro‐based industry, automobile industry, salt aerosols, industrial activities, and biomass burning).

Response of ozone to meteorology and atmospheric oxidation capacity in the Yangtze river Delta from 2017 to 2020

China implemented a stringent clean air initiative to improve air quality in 2013. By 2017, fine particulate matter (PM2.5) levels in the Yangtze River Delta (YRD) region dropped significantly, but ozone (O3) pollution surged, which aroused concerns about its adverse impact on crops, climate and human health. Due to the complex influences of atmospheric processes, the variation of O3 is highly nonlinear, making the control of O3 pollution highly challenging. It is urgent to deepen the understanding of the role of meteorological factors and atmospheric oxidation capacity (AOC) during O3 episodes. To provide references for O3 pollution control, the study used the Weather Research and Forecasting model (WRF) and the Community Multi-scale Air Quality model (CMAQ) simulations in the YRD from 2017 to 2020 to analyze the causes of severe O3 pollution. Observations revealed a slight decline in heavily O3 polluted days in the YRD region and the simulation results showed that O3 pollution was more prevalent at temperatures above 25 °C and relative humidity below 80%. The increase in volatile organic compounds (VOCs), hydroxyl radical (OH) and nitrate radical (NO3) was also more conducive to the formation of O3 pollution. For areas that are primarily VOC-limited areas, such as Shanghai, a stricter policy on VOC control is needed to reduce O3 pollution more effectively.

A Systematic Review for Indoor and Outdoor Air Pollution Monitoring Systems Based on Internet of Things

Global population growth and increasing pollution levels are directly related. The effect does not just apply to outdoor spaces. Likewise, the low indoor air quality is also having a negative impact on the health of the building residents. According to the World Health Organization, indoor air pollution is a leading cause of 1.6 million premature deaths annually. Tackling this public health issue, due to the direct relationship between air pollution levels and mortality and morbidity rates as well as overall comfort, is mandatory. Many companies have begun to build inexpensive sensors for use in Internet of Things (IoT)-based applications to pollution monitoring. The research highlights design aspects for sustainable monitoring systems including sensor types, the selected parameters, range of sensors used, cost, microcontrollers, connectivity, communication technologies, and environments. The main contribution of this systematic paper is the synthesis of existing research, knowledge gaps, associated challenges, and future recommendations. Firstly, the IEEE database had the highest contribution to this research (48.51%). The results showed that 87.1%, 66.3%, and 36.8% of studies focused on harmful gas monitoring, thermal comfort parameters, and particulate matter levels pollution, respectively. The most studied harmful gases were CO2, CO, NO2, O3, SO2, SnO2, and volatile organic compounds. The cost of the sensors was suitable for people with limited incomes and mostly under USD 5, rising to USD 30 for specific types. Additionally, 40.35% of systems were based on ESP series (ESP8266 and ESP32) microcontrollers, with ESP8266 being preferred in 34 studies. Likewise, IoT cloud and web services were the preferred interfaces (53.28%), while the most frequent communication technology was Wi-Fi (67.37%). Indoor environments (39.60%) were the most studied ones, while the share for outdoor environments reached 20.79% of studies. This is an indication that pollution in closed environments has a direct impact on living quality. As a general conclusion, IoT-based applications may be considered as reliable and cheap alternatives for indoor and outdoor pollution monitoring.

Advancing air quality forecasting in Abu Dhabi, UAE using time series models

This research enhances air quality predictions in Abu Dhabi by employing Autoregressive Integrated Moving Average (ARIMA) models on comprehensive air quality data collected from 2015 to 2023. We collected hourly data on nitrogen dioxide (NO2), particulate matter (PM10), and fine particulate matter (PM2.5) from 19 well-placed ground monitoring stations. Our approach utilized ARIMA models to forecast future pollutant levels, with extensive data preparation and exploratory analysis conducted in R. Our results found a significant drop in NO2 levels after 2020 and the highest levels of particulate matter observed in 2022. The findings of our research confirm the effectiveness of the models, indicated by Mean Absolute Percentage Error (MAPE) values ranging from 7.71 to 8.59. Additionally, our study provides valuable spatiotemporal insights into air pollution historical evolution, identifying key times and areas of heightened pollution, which can help in devising focused air quality management strategies. This research demonstrates the potential of ARIMA models in precise air quality forecasting, aiding in proactive public health initiatives and environmental policy development, consistent with Abu Dhabi’s Vision 2030.

Air Quality Monitoring Using Low-Cost Sensors in Urban Areas of Jodhpur, Rajasthan.

Air pollution poses a significant health hazard in urban areas across the globe, with India being one of the most affected countries. This paper presents environmental monitoring study conducted in Jodhpur, Rajasthan, India, to assess air quality in diverse urban environments. The study involved continuous indoor and outdoor air quality monitoring, focusing on particulate matter (PM2.5) levels, bioaerosols, and associated meteorological parameters. Laser sensor-based low-cost air quality monitors were utilized to monitor air quality and Anderson 6-stage Cascade Impactor & Petri Dish methods for bioaerosol monitoring. The study revealed that PM2.5 levels were consistently high throughout the year, highlighting the severity of air pollution in the region. Notably, indoor PM2.5 levels were often higher than outdoor levels, challenging the common notion of staying indoors during peak pollution. The study explored the spatial and temporal diversity of air pollution across various land-use patterns within the city, emphasizing the need for tailored interventions in different urban areas. Additionally, bioaerosol assessments unveiled the presence of pathogenic organisms in indoor and outdoor environments, posing health risks to residents. These findings underscore the importance of addressing particulate matter and bioaerosols in air quality management strategies. Despite the study's valuable insights, limitations, such as using low-cost air quality sensors and the need for long-term data collection, are acknowledged. Nevertheless, this research contributes to a better understanding of urban air quality dynamics and the importance of public awareness in mitigating the adverse effects of air pollution. In conclusion, this study underscores the urgent need for effective air quality management strategies in urban areas. The findings provide valuable insights for policymakers and researchers striving to address air pollution in rapidly urbanizing regions.

In-depth characterization of particulate matter in a highly polluted urban environment at the foothills of Himalaya-Karakorum Region.

In recent years, the rising levels of atmospheric particulate matter (PM) have an impact on the earth's system, leading to undesirable consequences on various aspects like human health, visibility, and climate. The present work is carried out over an insufficiently studied but polluted urban area of Peshawar, which lies at the foothills of the famous Himalaya and Karakorum area, Northern Pakistan. The particulate matter with an aerodynamic diameter of less than 10µm, i.e., PM10 are collected and analyzed for mineralogical, morphological, and chemical properties. Diverse techniques were used to examine the PM10 samples, for instance, Fourier transform infrared spectroscopy, x-ray diffraction, and scanning electron microscopy along with energy-dispersive x-ray spectroscopy, proton-induced x-ray emission, and an OC/EC carbon analyzer. The 24h average PM10 mass concentration along with standard deviation was investigated to be 586.83 ± 217.70µg/m3, which was around 13 times greater than the permissible limit of the world health organization (45µg/m3) and 4 times the Pakistan national environmental quality standards for ambient PM10 (150µg/m3). Minerals such as crystalline silicate, carbonate, asbestiform minerals, sulfate, and clay minerals were found using FTIR and XRD investigations. Microscopic examination revealed particles of various shapes, including angular, flaky, rod-like, crystalline, irregular, rounded, porous, chain, spherical, and agglomeration structures. This proved that the particles had geogenic, anthropogenic, and biological origins. The average value of organic carbon, elemental carbon, and total carbon is found to be 91.56 ± 43.17, 6.72 ± 1.99, and 102.41 ± 44.90µg/m3, respectively. Water-soluble ions K+ and OC show a substantial association (R = 0.71). Prominent sources identified using Principle component analysis (PCA) are anthropogenic, crustal, industrial, and electronic combustion. This research paper identified the potential sources of PM10, which are vital for preparing an air quality management plan in the urban environment of Peshawar.

Do air pollutants levels during the previous 48 hours predict out-of-hospital cardiac arrest incidence compared to same-day pollutants levels?

Abstract Funding Acknowledgements None. Introduction Association between air pollutants (APs) levels and out-of-hospital cardiac arrest (OHCA) at the same day of the measurements have been previously described. Whether APs levels measured during the previous 48 hours may predict OHCA occurrence is not known. Purpose To analyse whether same-day APs levels compared to APs levels during the previous 48 hours are associated with OHCA incidence. Methods OHCA admissions on five Cardiac Intensive Care Units of our city were assessed during September-2020 to December-2021. Free access data regarding particulate matter (PM10), carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) levels during the same period were also registered. APs measures occurred each hour daily at three different spots, delivering more than 31000 measurements. APs levels during the previous 48 hours and during the index day were compared between OHCA-free days (OHCA-Fd) and OHCA-days. Results 179 patients were included. Main demographic characteristics are shown on table 1. Daily OHCA incidence and APs mean levels/month are shown in Table 2. As shown at table 3 same-day higher NO2 and lower O3 levels were found on OHCA-days compared to OHCA-fd: 41 ±10 vs. 36 ±9 µg/m3, p=0.016 for NO2 and 45 ±13 vs. 51 ±11 µg/m3, p= 0.003 for O3. As shown on Table 2 same day mean O3 levels were also higher from April to September compared to the rest of the year (p&amp;lt;0.001). OHCA incidence was lower in this same period (p&amp;lt;0.001). There wasn’t any association between OHCA incidence and APs (NO2, O3, PM10 and CO) measured at any moment during the event's prior 48 hours. Conclusions Same-day higher NO2 and lower O3 levels were associated to a higher OHCA incidence in our city. On the other hand, association between OHCA and NO2, O3, PM10 or CO measurements during the previous 48 hours was not found in our cohort. Reducing production of NO2 (i.e., reducing fossil-fuel consumption) may reduce OHCA risk during that same day.

Long term trend of particulate matter in Philadelphia, Pennsylvania and its association with introduction of environmental policies

Since the 1970s, air quality has improved at the national level in the United States, coincident with the introduction of the Clean Air Act and other air pollution regulations at a greater frequency. We present a case study from Philadelphia, Pennsylvania—the sixth most populous city in the United States. The main objectives of this study are to analyze long-term trends of particulate matter (PM) from 1986 to 2021 in Philadelphia and to examine their association with the introduction of environmental policies relevant to air pollution at the federal, state, and local levels. We find that annual PM10 concentration decreased by 47% from 1986 to 2021 and annual PM2.5 concentration decreased by 31% from 2000 to 2021 in Philadelphia. We find that carbonaceous content (both elemental and organic carbon) has declined over the same period of 2000 to 2021, demonstrating its contribution to overall PM2.5 reduction in Philadelphia. In Philadelphia, high PM concentrations occur in the summer months; however, seasonal patterns have changed for PM10 in the last decade (2011–2020). Overall, PM reductions occurred over all seasons, with the greatest reductions occurring for PM2.5 during summer months and for PM10 during winter months. The Clean Air Act contributed to the creation of many regulatory policies that address unhealthy levels of PM. The introduction of various environmental policies that target the transportation sector has contributed to the reduction of PM levels in Philadelphia. Air quality would continue being improved by implementing such environmental policies specific to the emissions sectors.