Air Pollution Studies Research Articles

Examining modification of the associations between air pollution and birth outcomes by neighborhood deprivation in a North Carolina birth cohort, 2011-2015.

Evidence from studies of air pollutants and birth outcomes suggests an association, but uncertainties around geographical variability and modifying factors still remain. As neighborhood-level social characteristics are associated with birth outcomes, we assess whether neighborhood deprivation level is an effect measure modifier on the association between air pollution and birth outcomes in a North Carolina birth cohort. Using birth certificate data, all North Carolina residential singleton live births from 1 January 2011 to 31 December 2015 with gestational ages of 20-44 weeks (n = 566,799) were examined for birth defect diagnoses and preterm birth. Exposures were daily average fine particulate matter (PM2.5), daily 8-h maximum nitrogen dioxide (NO2), and daily 8-h maximum ozone (O3) modeled concentrations, and the modifier of interest was the neighborhood deprivation index (NDI). Linear binomial models were used to estimate the prevalence differences and 95% confidence intervals (CI) for the association between ambient air pollution and birth defect diagnoses. Modified Poisson regression models were used to estimate risk differences (RDs) and 95% CIs for air pollution and preterm birth. Models were stratified by the neighborhood deprivation index group (low, medium, or high) to assess potential modification by NDI. Approximately 3.1% of the study population had at least one birth defect and 8.18% were born preterm. For preterm birth, associations with PM2.5 and O3 did not follow a conclusive pattern and there was no evidence of modification by NDI. The associations between NO2 and preterm birth were generally negative across exposure windows except for a positive association with NO2 and preterm birth for high NDI [RD: 34.70 (95% CI 4.84-64.56)] for entire pregnancy exposure. There was no evidence of associations between pollutants examined and birth defects. There may be differences in the association between NO2 exposure and preterm birth by NDI but we did not observe any evidence of associations for birth defects. Our results support the public health protection afforded by reductions in air pollution, even in areas of neighborhood deprivation, but future research conducted in areas with higher levels of air pollution and evaluating the potential for modification by neighborhood deprivation level would be informative.

Concurrent Heat and Air Pollution Exposures among People Experiencing Homelessness.

Extreme heat and air pollution are important human health concerns; exposure can affect mental and physical well-being, particularly during periods of co-occurrence. Yet, the impacts on people are largely determined by underlying health conditions, coupled with the length and intensity of exposure. Preexisting adverse health conditions and prolonged exposure times are more common for people experiencing homelessness, particularly those with intersectional identity characteristics (e.g., disease, ability, age, etc.). Partially due to methodological limitations, such as data scarcity, there is a lack of research at the intersection of this at-risk population within the climate-health domain. We have three distinct objectives throughout this article: a) to advance critical discussions around the state of concurrent high heat and air pollution exposure research as it relates to people experiencing homelessness; b) to assert the importance of heat and air pollution exposure research among a highly vulnerable, too-often homogenized population-people experiencing homelessness; and c) to underline challenges in this area of study while presenting potential ways to address such shortcomings. The health insights from concurrent air pollution and heat exposure studies are consequential when studying unhoused communities who are already overexposed to harmful environmental conditions. Without holistic data sets and more advanced methods to study concurrent exposures, appropriate and targeted prevention and intervention strategies cannot be developed to protect this at-risk population. We highlight that a) concurrent high heat and air pollution exposure research among people experiencing homelessness is significantly underdeveloped considering the pressing human health implications; b) the severity of physiological responses elicited by high heat and air pollution are predicated on exposure intensity and time, and thus people without means of seeking climate-controlled shelter are most at risk; and c) collaboration among transdisciplinary teams is needed to resolve data resolution issues and enable targeted prevention and intervention strategies. https://doi.org/10.1289/EHP13402.

Neighborhood-Level Air Pollution Monitoring and Analysis in Massachusetts

Air pollution is a significant environmental issue with far-reaching consequences for both human health and the planet. It arises from the emission of harmful pollutants, such as particulate matter, ozone, and nitrogen dioxide, into the atmosphere. These pollutants originate from various sources, including vehicles, industrial facilities, and power plants. The impacts of air pollution are wide-ranging, encompassing respiratory infections, cardiovascular disease, cancer, harm to ecosystems, and contributions to climate change. The study of air pollution is a multidisciplinary field that draws upon knowledge from physics, chemistry, biology, and engineering. Understanding the causes and effects of air pollution is crucial for developing effective strategies to mitigate its harmful effects. Physics, in particular, plays a vital role in this field by providing the tools and techniques required to measure and comprehend the behavior of pollutants. By examining air pollution, we gain insights into the factors driving this problem and can devise measures to reduce pollution levels. Moreover, this knowledge empowers individuals and societies to make informed decisions about minimizing exposure to pollutants and formulating policies and regulations that safeguard both human well-being and the environment. In our study conducted in the central Massachusetts region, we investigated the status of air pollution using a combination of Environmental Protection Agency (EPA) monitoring sites and hand-held sensors. While the EPA sites offer longterm monitoring data, hand-held devices’ flexible and affordable nature allowed us to explore air quality at the local neighborhood and street levels. By utilizing these tools, we assessed the spatial and temporal variations in air pollution within the city, aiding in the identification of localized hotspots. Such information is valuable for targeting specific areas requiring interventions and further understanding the dynamics of pollution distribution.

EVALUATION OF DUST EMISSION RATE FROM LANDFILL MINING ACTIVITIES

Mining operations are one of the most significant sources of particulate emissions in the atmosphere. Landfill mining (LFM) process activities, including excavation, screening, shredding, and equipment handling, have the potential to emit particulate matter into the environment as short-term episodic emissions during operational periods. Previous investigations show that LFM activities can potentially cause human health and environmental impacts through exposure of these emissions. This paper evaluates the dust emission rate of such activities to understand factors responsible for higher emissions rate and determine where any pressure points exist in order to mitigate risk. Nine empirical formulas were adopted from surface mining activities, including point, line, and area sources of activity. Parameters identified in the equations were adjusted to LFM application conditions. From emission results, it is observed that point source activities were the major sources of emission. The study area was divided into multiple phases and one phase cumulative for the maximum/average/minimum point sources emissions over the lifetime of the landfill mining operation calculated in this study are approximately 5.04 tonnes (t) / 3.23 (t) / 1.61 (t), respectively. However, the one phase cumulative for the maximum/average/minimum line sources emissions over the lifetime of the landfill mining operation are approximately 100.8 (kg/m) / 40.32 (kg/m) / 20.16 (kg/m), respectively. Mitigation measures to control high emission rate of LFM related activity, such as utilising tankers or bowsers to spray water around the LFM area, to control airborne emissions, should be considered. The results of this research are expected to inform air dispersion modelling for environmental impact assessment studies of air pollution.

Evaluating low-cost monitoring designs for PM2.5 exposure assessment with a spatiotemporal modeling approach

Determining the most feasible and cost-effective approaches to improving PM2.5 exposure assessment with low-cost monitors (LCMs) can considerably enhance the quality of its epidemiological inferences. We investigated features of fixed-site LCM designs that most impact PM2.5 exposure estimates to be used in long-term epidemiological inference for the Adult Changes in Thought Air Pollution (ACT-AP) study. We used ACT-AP collected and calibrated LCM PM2.5 measurements at the two-week level from April 2017 to September 2020 (N of monitors [measurements] = 82 [502]). We also acquired reference-grade PM2.5 measurements from January 2010 to September 2020 (N = 78 [6186]). We used a spatiotemporal modeling approach to predict PM2.5 exposures with either all LCM measurements or varying subsets with reduced temporal or spatial coverage. We evaluated the models based on a combination of cross-validation and external validation at locations of LCMs included in the models (N = 82), and also based on an independent external validation with a set of LCMs not used for the modeling (N = 30). We found that the model's performance declined substantially when LCM measurements were entirely excluded (spatiotemporal validation R2 [RMSE] = 0.69 [1.2 μg/m3]) compared to the model with all LCM measurements (0.84 [0.9 μg/m3]). Temporally, using the farthest apart measurements (i.e., the first and last) from each LCM resulted in the closest model's performance (0.79 [1.0 μg/m3]) to the model with all LCM data. The models with only the first or last measurement had decreased performance (0.77 [1.1 μg/m3]). Spatially, the model's performance decreased linearly to 0.74 (1.1 μg/m3) when only 10% of LCMs were included. Our analysis also showed that LCMs located in densely populated, road-proximate areas improved the model more than those placed in moderately populated, road-distant areas.

Urban Air-Quality Estimation Using Visual Cues and a Deep Convolutional Neural Network in Bengaluru (Bangalore), India.

Mobile monitoring provides robust measurements of air pollution. However, resource constraints often limit the number of measurements so that assessments cannot be obtained in all locations of interest. In response, surrogate measurement methodologies, such as videos and images, have been suggested. Previous studies of air pollution and images have used static images (e.g., satellite images or Google Street View images). The current study was designed to develop deep learning methodologies to infer on-road pollutant concentrations from videos acquired with dashboard cameras. Fifty hours of on-road measurements of four pollutants (black carbon, particle number concentration, PM2.5 mass concentration, carbon dioxide) in Bengaluru, India, were analyzed. The analysis of each video frame involved identifying objects and determining motion (by segmentation and optical flow). Based on these visual cues, a regression convolutional neural network (CNN) was used to deduce pollution concentrations. The findings showed that the CNN approach outperformed several other machine learning (ML) techniques and more conventional analyses (e.g., linear regression). The CO2 prediction model achieved a normalized root-mean-square error of 10-13.7% for the different train-validation division methods. The results here thus contribute to the literature by using video and the relative motion of on-screen objects rather than static images and by implementing a rapid-analysis approach enabling analysis of the video in real time. These methods can be applied to other mobile-monitoring campaigns since the only additional equipment they require is an inexpensive dashboard camera.

Hygienic study and forecast of atmospheric air pollution with sulfur compounds in the areas of sulfide-containing tailings

This article is devoted to the topical problem of the release into the atmosphere of mixtures of sulfur compounds from decommissioned facilities for the disposal of waste from the processing of sulfide ores of non-ferrous and noble metals. The purpose of the research was a hygienic study and forecast of atmospheric air pollution by volatile sulfur compounds emitted by decommissioned waste disposal facilities for processing sulfide ores of non-ferrous and precious metals, for information support of the system of medical and preventive technologies in mining regions. The objects of the study were two decommissioned tailing dumps of enterprises for the processing of gold ores. The assessment of the actual pollution of the surface layer of the atmosphere with dimethyl sulfide, dimethyl sulfoxide, carbon disulfide, sulfur dioxide and hydrogen sulfide was carried out according to under-flare observations. Non-linear models given by second and third order polynomials were used to predict pollution levels. A theoretical analysis of the in ‑ formation presented in the scientific literature on the interaction of structural elements of natural and technical systems that make up the mechanism of atmospheric pollution at the post-operational stage of mining technogenesis has been carried out. Based on a systematic analysis of under-flare observations conducted in the areas of location of two tailings of gold ore production, lists of priority sulfur compounds to be controlled, distinctive features of zonal atmospheric pollution for different forms of terrain adjacent to the sources were identified, non-linear models of the distribution of concentrations of priority pollutants in the surface were constructed. layer of the atmosphere, allowing a probabilistic quantitative assessment of pollution halos and inhalation risks to public health. The scientific information obtained as a result of a comprehensive study is intended for information support of the system of medical and preventive technologies in mining regions.

Improved O3 predictions in China by combining chemical transport model and multi-source data with machining learning techniques

Accurate ozone (O3) predictions is crucial for assessing its impact on public health and developing effective prevention and control measures. While ground-based observations are considered highly accurate, the limited and uneven spatial coverage poses challenges. Air quality models can provide complete spatiotemporal coverage but are subject to biases due to simplifications in physicochemical mechanisms and uncertainties associated with emission and meteorology inputs. In this study, we aimed to improve the accuracy of the predicted O3 concentrations from the Community Multiscale Air Quality (CMAQ) model by using the machine learning techniques. First, we compared three machine learning algorithms, namely Light Gradient Boosting Machine (LightGBM), Random Forest, and eXtreme Gradient Boosting. Results showed that LightGBM exhibited the highest correlation coefficient (R) of 0.84, making it the preferred algorithm for further analysis. Subsequently, two multi-source data prediction models based on LightGBM were constructed to improve the accuracy of predicted daily maximum 8-h O3 (O3-Max8h). The first model, referred to as LGBR, utilized the predicted air pollutant concentrations and meteorological fields as input variables, while the second model, named LGBR-CHAP, incorporated ChinaHighAirPollutants (CHAP) O3-Max8h as an additional input variable. Validation results demonstrated significant improvements in the LGBR and LGBR-CHAP models compared to the original CMAQ model. On the daily scale, the root mean square error and mean bias of the predicted O3-Max8h decreased by 3.15 and 2.07 μg/m3, respectively, for the LGBR model, while they decreased by 5.61 and 4.18 μg/m3, respectively, for the LGBR-CHAP model. On the monthly scale, the R of the original CMAQ model varied from 0.2 in the March to 0.91 in June, and LGBR improved all months (0.4–0.92) as did LGBR-CHAP (0.5–0.94). Spatially, the O3-Max8h simulated by the original CMAQ model is better in eastern China but less skillful in western China. After optimization by the LGBR and LGBR-CHAP models, the national average R improved from 0.77 to 0.83 and 0.88, respectively. The LGBR-CHAP model exhibited better predictive capacity than the LGBR model and was subsequently employed to generate high-resolution (10 km × 10 km) and full-coverage (100%) O3-Max8h data. This dataset will prove valuable for future air pollution and health studies.

Statistic Study of Gaseous Air Contamination in the City of Svishtov, Bulgaria

Polluted air has a negative impact on human health and plant and animal life. There are two types of sources of air pollution: human activity (transport, heating, industry and others) and natural phenomena such as natural fires, volcanic activity, deforestation and others. Substances that are the most common air pollutants are dust particles, carbon and nitrogen dioxides, carbon and nitrogen oxides, hydrocarbons; aldehydes; radioactive substances, heavy metals, sulfur dioxide, ozone, etc. Gaseous air pollutants of primary concern in urban environments include sulfur dioxide, nitrogen dioxide, and carbon monoxide. Sulfur dioxide is a colorless gas that is a product of volcanic eruptions and various industrial processes. It’s usually released when coal and oil are burned. Nitrogen oxides, especially nitrogen dioxide, are poisonous gases with a pungent, suffocating odor. There are many regulations aimed at air pollution and pollution monitoring for all European countries. Bulgaria, as part of the EU, must comply with all these regulations. There are still many places and periods of time in Bulgaria over the years with air pollution greater than daily norms. The present study is dedicated to the study of air pollution with gaseous substances in the area of Svishtov, Bulgaria. The town of Svishtov is located in Northern Bulgaria, on the banks of the Danube River, where the southernmost point of the river along its entire length is located. A statistical analysis of the level of air pollution in Svishtov based on official data from the monitoring station in the city is presented. The measurements cover the period 2021 - 2023. All research results are presented graphically and commented.

Household Air Pollution and Child Lung Function: The Ghana Randomized Air Pollution and Health Study.

Rationale: The impact of a household air pollution (HAP) stove intervention on child lung function has been poorly described. Objectives: To assess the effect of a HAP stove intervention for infants prenatally to age 1 on, and exposure-response associations with, lung function at child age 4. Methods: The Ghana Randomized Air Pollution and Health Study randomized pregnant women to liquefied petroleum gas (LPG), improved biomass, or open-fire (control) stove conditions through child age 1. We quantified HAP exposure by repeated maternal and child personal carbon monoxide (CO) exposure measurements. Children performed oscillometry, an effort-independent lung function measurement, at age 4. We examined associations between Ghana Randomized Air Pollution and Health Study stove assignment and prenatal and infant CO measurements and oscillometry using generalized linear regression models. We used reverse distributed lag models to examine time-varying associations between prenatal CO and oscillometry. Measurements and Main Results: The primary oscillometry measure was reactance at 5 Hz, X5, a measure of elastic and inertial lung properties. Secondary measures included total, large airway, and small airway resistance at 5 Hz, 20 Hz, and the difference in resistance at 5 Hz and 20 Hz (R5, R20, and R5-20, respectively); area of reactance (AX); and resonant frequency. Of the 683 children who attended the lung function visit, 567 (83%) performed acceptable oscillometry. A total of 221, 106, and 240 children were from the LPG, improved biomass, and control arms, respectively. Compared with control, the improved biomass stove condition was associated with lower reactance at 5 Hz (X5 z-score: β = -0.25; 95% confidence interval [CI] = -0.39, -0.11), higher large airway resistance (R20 z-score: β = 0.34; 95% CI = 0.23, 0.44), and higher AX (AX z-score: β = 0.16; 95% CI = 0.06, 0.26), which is suggestive of overall worse lung function. The LPG stove condition was associated with higher X5 (X5 score: β = 0.16; 95% CI = 0.01, 0.31) and lower small airway resistance (R5-20 z-score: β = -0.15; 95% CI = -0.30, 0.0), which is suggestive of better small airway function. Higher average prenatal CO exposure was associated with higher R5 and R20, and distributed lag models identified sensitive windows of exposure between CO and X5, R5, R20, and R5-20. Conclusions: These data support the importance of prenatal HAP exposure on child lung function. Clinical trial registered with www.clinicaltrials.gov (NCT01335490).

Unveiling the air pollution tapestry in China: A comprehensive assessment of spatiotemporal variations through geographically and temporally weighted regression

Study of air pollution informs environmentally-conscious policies and urban planning by government and businesses, leading to more scientific decision-making. This paper comprehensively analyzes the spatiotemporal correlation of six air pollutants (PM2.5, PM10, SO2, NO2, CO, and O3) at the national level of China from 2015 to 2021. The temporal changes in environmental air pollutants are examined through statistical data analysis. Secondly, the annual and quarterly spatial distribution characteristics of air pollutants are analyzed by utilizing global and local spatial autocorrelation analysis and visualizing pollutant concentration data. To quantify the spatiotemporal correlation information obtained from the data, we proposed a novel framework based on the Geographically and Temporally Weighted Regression (GTWR) model to provide a local approach for detecting the spatiotemporal correlation between urban air pollutants on a large scale. The framework's experimental results showed that the significant correlation between urban air pollutants in time and space ranged from 0.45 to 0.9, which is better than traditional correlation algorithm, and the degree of local spatiotemporal correlations can be explained by spatiotemporal coefficients. We find noticeable differences in the correlations between different pollutants and geographical boundaries in the spatial dimension. Furthermore, in terms of spatiotemporal location, PM2.5, PM10, NO2, SO2, and CO exhibit positive correlations with each other, while O3 shows both positive and negative correlations with other pollutants. This study offers an important reference for air quality monitoring and prediction, contributing to the improvement of accuracy and timeliness of air quality warnings.

Analysis of Air Pollutants and Their Potential Sources in Eastern Xinjiang, Northwestern Inland China, from 2018 to 2022

Air pollution in the developed regions of eastern China has been intensively investigated in the past decade. However, there is a relative dearth of air pollution studies on the northwest of inland China (e.g., Xinjiang). In this work, hourly measurement data of six criteria air pollutants (PM2.5, PM10, CO, NO2, O3, and SO2) for the past five years (2018–2022) from Hami and Turpan cities of eastern Xinjiang were analyzed to reveal air pollution characteristics and the distribution of potential sources. Hami and Turpan had the highest AQI values in winter due to increased coal combustion for domestic heating and unfavorable meteorological conditions. The slight elevations of AQI values in spring were caused by frequent dust storms. PM10 was the most frequent main pollutant in both Hami (63.1%) and Turpan (74.1%), followed by PM2.5 and O3. Except for O3, PM2.5, PM10, SO2, NO2, and CO exhibited a generally decreasing pattern in annual average values. But the annual average concentrations of PM10 in Hami (83.5 μg·m−3) and Turpan (139 μg·m−3) in 2022 were still higher than those in eastern China. Diurnal and monthly variations of the six criteria pollutants were influenced by a combination of emission sources and meteorological conditions. The air masses in eastern Xinjiang mainly originated from the west and north and were affected by both inter-regional and intra-regional transport. Analysis of the distribution of potential sources showed that local emissions strongly impacted particulate matter pollution in winter, while regional transport played a dominant role in other seasons. O3 showed a broad distribution of potential sources across all four seasons. Considering that the trend that O3 pollution increased year by year, eastern Xinjiang might face a similar pollution situation as eastern China, i.e., the combined pollution of particulate matter and O3.

Monitoring gaseous pollutants using passive sampling in the Philadelphia region

ABSTRACT Air pollution can have deleterious impacts on human health and the environment. Historically, air pollution studies have focused more on cities. However, it is also important to consider the impact on large suburban populations living closer to the major cities. In this study, nitrogen oxides (nitrogen dioxide and nitric oxide), sulfur dioxide, ozone, and ammonia concentrations were measured from fifteen sites in the Greater Philadelphia area, Pennsylvania, USA using Ogawa passive samplers from September 2021 to May 2022. The fall season had the highest mean NOx concentrations (11.03 ± 4.51 ppb), and spring had the highest mean O3 concentration (18.65 ± 6.71 ppb) compared to other seasons. NOx concentrations were higher at suburban (30.43 ± 33.79 ppb) and urban sites (22.49 ± 12.54 ppb) compared to semi-rural sites (11.08 ± 9.20 ppb). SO2 was not detected in most of the measurements. The positive statistically significant correlation between NO and NH3 in urban (R2 = 0.33, p-value <0.05) and suburban sites (R2 = 0.37, p-value <0.05) during winter and spring, respectively, suggests a high attribution of traffic emissions to NH3 at urban and suburban sites. Influence of traffic emissions on air pollutant values for the study region is also supported by similar NOx concentrations between suburban and urban sites as well as decreasing NO2/NOx ratios with increased distance from expressways. This study shows that passive sampling can be effectively used for assessing spatial and seasonal variations in air pollutants within an area of diverse land use. Implications: This study presents the findings of temporal and seasonal patterns for nitrogen dioxide, nitric oxide, tropospheric ozone, and ammonia at urban, suburban, and semi-rural areas of the greater Philadelphia region. The main objective of the study is to monitor air pollution in suburban and semi-rural areas which are not monitored for air pollution. We monitored from a total of fifteen sites in three seasons to assess air pollution in suburban and semi-rural areas near the major city in the United States – Philadelphia. The findings are important to learn how air quality is affected in suburban and semi-rural areas near the major city. The study also shows the useful application of inexpensive passive sampling technique for measuring air pollution.

Complex study of air pollution based on tree species in Vienna

Plants are especially useful as biological indicators to assess air pollution and the effects of urbanisation. The aim of this study was to investigate tree species’ sensitivity to air pollution using the Air Pollution Tolerance Index (APTI), the dust content (PM10) of leaves, and the heavy metal concentration of leaves. Sampling sites were in a city park (urban area), a location on the edge of the city (suburban area), and a forested location (rural area) along an urbanisation gradient in the city of Vienna in Austria. Leaf samples were collected from Acer platanoides, Fraxinus excelsior, and Quercus robur tree species. The APTI was determined by measuring the content of relative water ascorbic acid and chlorophyll, and the pH of leaf extracts. We found significant difference among species in their relative water content, pH, and content of ascorbic acid and chlorophyll Additionally, we found significant differences in chlorophyll content and pH based on sampling site. The highest chlorophyll content was found in A. platanoides. There was a significant difference among species in Al, Ba, Cr, Ni, and Zn concentrations in each stage of urbanisation. Based on APTI values, the studied species were sensitive indicators of air pollution; thus, they are useful bioindicator species, and they are suitable for air pollution monitoring. We demonstrated the effects of urbanisation with these bioindicator species with elevated PM10, Al, Ba, Cu, Fe, Sr, and Zn concentrations in urban area samples.Graphical abstract

A Review Paper on Study of Air Pollution and its Control Measures

Abstract: In this paper, we walked you through the definition of air pollution and it's introduction, and then classified the various causes of air pollution and then it's different hazardous effects on us. The Pollution refers to any unwanted change that occurs in the environment. The air pollution is caused by pollutants that are harmful substances. Air pollution refers to the contamination of air due to the presence of chemicals, harmful gases, and dangerous elements. These include methane, ammonia, nitrogen, carbon monoxide, sulphur dioxide, and so on.

Diurnal time representation of MODIS, VIIRS, MISR, and AHI over Asia and Oceania

Retrievals of satellite aerosol optical depth (AOD) products are used for studies of air pollution, energy balance, and climate change. However, few studies have evaluated the diurnal time representation (DTR) of satellite AOD products. In this study, we proposed a statistically valid threshold method to investigate the DTR of Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), Multi-angle Imaging Spectroradiometer (MISR), and Advanced Himawari Imager (AHI) AOD products by comparing their retrievals to the daily AERONET AOD values and explored the ability of AHI and AERONET to capture daily aerosol variability at 54 sites in Asia and Oceania from July 2015 to December 2019. For polar-orbiting satellites, based on collocations with AERONET measurements, MISR-derived AOD showed high consistency with daily average AERONET AOD, with a correlation coefficient (R) of 0.94 and 80.85% matchups falling into the envelopes of expected error (EE, ± (0.05 + 0.15 × AODAERONET)). Compared with single MODIS products, a combination of satellite data from the Terra and Aqua MODIS dark target (DT) algorithm might obtain a dataset with better DTR (R = 0.94 and %Within EE = 72.10). The satellite-retrieved AOD did a good job to represent the daily mean AERONET AOD, because AERONET AOD averaged within a ± 30 min time window of different satellite overpasses closely mirrored the daily mean AERONET AOD. Furthermore, the seasonal average diurnal variations (SADV) of AERONET AOD were changing insignificant, except for the urban coverage area in the Southern Hemisphere and equatorial climate. For geostationary satellite, the DTR of AHI was relatively poorer in comparison to the other sensors (R = 0.84, %Within EE = 58.00), and AHI AOD was seriously underestimated in South Asia and overestimated in Oceania. Besides, the SADVs of AHI AOD presented several patterns summarized as wave type, unimodal type, stable fluctuation type, and other types, which meant that AHI AOD retrievals did not accurately capture the diurnal variability of aerosols. This study provides a reference for the aerosol community to select satellite AOD products with appropriate DTR.

Study of Air Pollution by NOx from Petrol Fuels at Four Stations in Dakar–Senegal by Determining Nitrogen Content

NOX emissions are a health hazard, and are increasingly regulated in cars. NOx also contributes to the formation of acid rain and the eutrophication of ecosystems. Nitrogen oxides are a family of molecules including nitrogen monoxide (NO) and nitrogen dioxide (NO2). These gases are formed in the engine during high-temperature fuel combustion, and play a role in the formation of fine particles in ambient air. In this article, the aim is to determine the nitrogen content of gasoline samples taken at four gasoline stations of the most representative groups in terms of distribution of light petroleum products in order to assess the environmental impact due to greenhouse gas emissions resulting from the use of gasoline by vehicles and to reduce the nitrogen content in the form of nitrogen oxides NOx. After an introduction, we presented the material and the method used, followed by the results of our analyses and a discussion of the results obtained before concluding.

Adsorption studies of air pollutants on α-SbP with density functional theory

Adsorption studies of air pollutants on α-SbP with density functional theory

PM2.5-Bound Fatty Acids and Oxidative Potential: A Year-Round Study in Chiang Mai, Upper Northern Thailand

Air pollution study mostly targeted in components of fine particulate matter (PM2.5, particulate matter with aerodynamic diameter ≤ 2.5 µm) but their health implication is still scarce. Fatty acids could exhibit surfactant properties of PM2.5. We investigated the oxidative potential (OP) of PM2.5-bound fatty acids in Chiang Mai, upper northern Thailand: a year-round study in 2021. A dithiothreitol (DTT), an acellular assay, was used to evaluate the OP of eighty-nine PM2.5 filters collected from January to December 2021. A series of PM2.5-bound fatty acids i.e., C14:0–C30:0 fatty acids were determined by gas chromatography/mass spectrometry (GC/MS). Total fatty acids (C14:0–C30:0 and C18:1, C18:2) accounted for 498.7 ± 196.1 and 206.9 ± 112.8 ng/m³ in high biomass burning (HBB) and low biomass burning (LBB) periods, respectively. Mean values of OPDTTv (OP normalized by air volume of sampling) in HBB and LBB periods were 0.13 ± 0.06 and 0.03 ± 0.03 nmol/min/m³, respectively. Long chain saturated fatty acids (C22:0-26:0) mostly showed moderately significant correlation with OPDTTv (r = 0.33-0.47, P &lt; 0.01) in HBB period but showed no significant correlation in LBB period. Meanwhile, linoleic acid (C18:2) was the only individual compound among unsaturated fatty acids that significantly correlated with OPDTTv (r = 0.39, P &lt; 0.01). These findings suggest that PM2.5-bound fatty acids, particularly during open-biomass burning period, contribute to variations in oxidative potential, potentially influencing the health risks associated with PM2.5 exposure.

Spatio-temporal exposure assessment of particulate matter pollution in auto-rickshaw drivers in Chennai, India

Rapid urbanization and population growth have put a lot of stress on the urban infrastructure. The number of vehicles, particularly the three wheeled auto-rickshaws, have increased drastically in the past decade in developing countries like India, Pakistan, Bangladesh, etc. The distinct design of these vehicles make the drivers and the passengers vulnerable to the traffic emissions and resuspended dust. With the objective of assessing the exposure of auto-rickshaw drivers to particulate matter in Chennai, this study used an auto-rickshaw to monitor the particulate matter (PM10, PM2.5 and PM1) concentrations during weekday peak and non-peak hours, across 15 administrative zones of Chennai, India. The particle size distribution profile for each zone was identified to understand the source contribution. Low PM2.5/PM10 ratio (<0.15) was observed in the western parts of Chennai consisting residential and commercial zones (zone 10 and 11), and high PM1/PM2.5 ratio (>0.8) in the highly industrialized northern parts of Chennai (zone 4 and 5). The coarse particles of size 31 μm and 34 μm were predominant in most of the zones. Northern and central Chennai had 100 % exceedances in PM10 and PM2.5 during both peak and non-peak hour. Auto-rickshaw drivers were exposed to a maximum of 4.26 μg/km and 0.12 μg/km of PM2.5 and PM1, respectively. Based on these results, we suggest the auto-rickshaw drivers and passengers to follow necessary precautions like wearing masks, and the policymakers to develop control measures based on such spatial studies of air pollution across the entire city.