Assessment Of Air Pollution Research Articles

Assessing the nonlinearity of wintertime PM2.5 formation in response to precursor emission changes in North China with the adjoint method

Abstract While China’s clean air actions implemented since 2013 have been effective in mitigating PM2.5 air pollution, the large emission reductions during the COVID-19 lockdown period in early 2020 did not similarly alleviate PM2.5 pollution in North China, reflecting a distinct nonlinear chemical response of PM2.5 formation to emission changes. Here we apply emission-concentration relationships for PM2.5 diagnosed using the adjoint approach to quantitatively assess how chemical nonlinearity affects PM2.5 over Beijing in February 2020 in response to two emission reduction scenarios: the COVID-19 lockdown and 2013–2017 emission controls. We find that, in the absence of chemical nonlinearity, the COVID-19 lockdown would decrease PM2.5 in Beijing by 17.9 μg m–3, and the 2013–2017 emission controls resulted in a larger decrease of 54.2 μg m–3 because of greater reductions of SO2 and primary aerosol emissions. Chemical nonlinearity offset the decrease for Beijing PM2.5 by 3.4 μg m–3 during the lockdown due to enhanced sensitivity of aerosol nitrate to NO x emissions, but enhanced the efficiency of 2013–2017 emission controls by 11.9 μg m–3 due to the weakened heterogeneous reaction of sulfate. Such nonlinear chemical effects are important to estimate and consider when designing or assessing air pollution control strategies.

The Assessment and Analysis of Environmental Air Pollution by Waste Emission of Navy Ships (A Conceptual Thinking)

The Assessment and Analysis of Environmental Air Pollution by Waste Emission of Navy Ships (A Conceptual Thinking)

Assessment of Urban air pollution by PM10 and NO2 and associated impacts and risks through computational analysis in Kigali, Rwanda

Assessment of Urban air pollution by PM10 and NO2 and associated impacts and risks through computational analysis in Kigali, Rwanda

Interaction assessment of air pollutants and environmental variables in Aures, Algeria

Interaction assessment of air pollutants and environmental variables in Aures, Algeria

Spatiotemporal distribution of oxidative potential in PM2.5 and its key components across six Chinese cities

Increasing evidence has supported that oxidative potential (OP) serves as a crucial indicator of health risk of exposure to PM2.5 over mass concentration. However, there is a lack of comparative studies across multiple cities, particularly on a fine temporal scale. In this study, we aim to investigate daily variation of ambient PM2.5 OP through simultaneous samplings in six Chinese cities for one year. Results showed that more than 60 % of the sampling days exhibited non-zero ranking difference between volume-normalized oxidative potential (OPv) and mass concentration among the six cities. Key components contributing to OPv inculde Mn, NO3-, and K+, followed by Ca2+, Al, SO42-, Cl-, Fe, and NH4+. Based on these chemical components, we developed a stepwise multivariable linear regression model (R2: 0.71) for OPv prediction. The performance of the model is comparable to both species- and sources-based ones in the literature. These findings suggest that a relatively lower daily-averaged mass concentration of PM2.5 does not necessarily indicate a lower oxidative risk. Future studies and policy developments on health benefits should also consider OPv rather than mass concentration alone. Priority could be given to sources/species that contribute significantly to oxidative potential of ambient PM2.5. SynopsisThis study highlights inclusion of oxidative potential as a complementary metric for air pollution assessment and control.

AIRS and MODIS Satellite-Based Assessment of Air Pollution in Southwestern China: Impact of Stratospheric Intrusions and Cross-Border Transport of Biomass Burning

The exacerbation of air pollution during spring in Yunnan province, China, has attracted widespread attention. However, many studies have focused solely on the impacts of anthropogenic emissions while ignoring the role of natural processes. This study used satellite data spanning 21 years from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Atmospheric Infrared Sounder (AIRS) to reveal two natural processes closely related to springtime ozone (O3) and PM2.5 pollution: stratospheric intrusions (SIs) and cross-border transport of biomass burning (BB). We aimed to assess the mechanisms through which SIs and cross-border BB transport influence O3 and PM2.5 pollution in Southwestern China during the spring. The unique geographical conditions and prevalent southwest winds are considered the key driving factors for SIs and cross-border BB transport. Frequent tropopause folding provides favorable dynamic conditions for SIs in the upper troposphere. In the lower troposphere, the distribution patterns of O3 and stratospheric O3 tracer (O3S) are similar to the terrain, indicating that O3 is more likely to reach the surface with increasing altitude. Using stratospheric tracer tagging methods, we quantified the contributions of SIs to surface O3, ranging from 6 to 31 ppbv and accounting for 10–38% of surface O3 levels. Additionally, as Yunnan is located downwind of Myanmar and has complex terrain, it provides favorable conditions for PM2.5 and O3 generation from cross-border BB transport. The decreasing terrain distribution from north to south in Yunnan facilitates PM2.5 transport to lower-elevation border cities, whereas higher-elevation cities hinder PM2.5 transport, leading to spatial heterogeneity in PM2.5. This study provides scientific support for elucidating the two key processes governing springtime PM2.5 and O3 pollution in Yunnan, SIs and cross-border BB transport, and can assist policymakers in formulating optimal emission reduction strategies.

Optimising the network of air quality monitoring stations

This study explores the application of genetic algorithms (GAs) for optimizing the network of air quality monitoring stations. Recognizing the complexity of accurately assessing air pollution levels across diverse urban and rural landscapes, the research focuses on finding the most effective station placements to maximize coverage and data fidelity while minimizing costs. The methodology entails simulating natural selection processes, including selection, crossover, and mutation, to evolve a population of potential solutions. Each candidate solution in the population represents a unique configuration of monitoring stations. A fitness function evaluates the efficiency of each configuration based on criteria such as population coverage, proximity to pollution sources, and installation and operational expenses. The research employs a genetic algorithm developed in Python, which iteratively refines the population of solutions over thousands of generations. The algorithm's performance is assessed through experimental validation, with an emphasis on the adaptability of the approach to accommodate various environmental, economic, and regulatory constraints. Results indicate that GAs can effectively balance multiple optimization objectives, leading to a network design that is both cost-efficient and comprehensive in its monitoring capabilities. The outcome of the study is an optimized network that significantly improves upon the initial state in terms of coverage and cost-effectiveness. The study concludes that genetic algorithms offer a promising avenue for addressing the challenges of air quality monitoring network design. The flexibility and global search capabilities of GAs make them suitable for the complex, multi-objective nature of the task. Moreover, the findings suggest potential for further improvements and applications of GAs in environmental monitoring and other complex systems optimization scenarios.

The UNEA 6 Resolution on Air Quality acknowledges the Integrated Assessment of Air Pollution and Climate Change for Sustainable Development in Africa

In a major boost to air quality and sustainable development in Africa, a new UNEA 6 Resolution on Air Quality has acknowledged the Integrated Assessment of Air Pollution and Climate Change for Sustainable Development in Africa. The new development came in 2023, when the Integrated Assessment of Air Pollution and Climate Change for Sustainable Development in Africa (UNEP, 2023), was published by a partnership of the African Union, Climate and Clean Air Coalition and the United Nations Environment Programme. The assessment was developed with contributions from over 100 authors across Africa, in a process supported by the Stockholm Environment Institute (Kaudia et al. 2022). The Assessment recommended for an Africa Clean Air Program (ACAP) to provide a key rallying point for African multilateral institutions, the Regional Economic Communities (RECs) and Member States, and development partners to work collaboratively to implement the recommendations from the assessment and deliver multiple benefits of clean air for Africa.

Machine learning models application for spatiotemporal patterns of particulate matter prediction and forecasting over Morocco in north of Africa

Atmospheric air pollution exposure raises morbidity and mortality rates and is a major cause of the world's illness burden. In this context, we explored spatial and temporal trends in particulate matter PM10 from 2003 to 2020 over Morocco to assess air pollution exposure. We use the capabilities of ML models to study PM10 trends using 26 predictor variables, including meteorological parameters, volatile organic compounds, atmospheric oxidants, and aerosol optical depth data from the Copernicus Atmosphere Monitoring Service (CAMS). For this purpose, three ML models were built: Multiple Linear Regression (MLR), Random Forest Regression (RFR), and Generalized Additive Model (GAM). To match and optimize these models, a set of ML algorithms has been coupled with each model. The results show all these models are highly accurate in predicting and forecasting PM10 total column trends. Cross-validation showed that GAM had better prediction ability for the PM10 total column with R2 = 0.994 and a very low root mean squared error (RMSE) not exceeding 0.046 × 10−16 kg/m2. The GAM model showed much higher predictive ability and lower bias than the other models. This finding can be explained by the advantages of GAMs, including their ability to capture complex and non-linear patterns in the data, making them particularly useful when relationships are not easily represented by linear models. This study has presented a comprehensive methodology for predicting the spatiotemporal variability of PM10. The proposed methodology holds potential applicability across all regions, facilitating the generation of high-resolution PM10 monitoring and the establishment of systems for the early detection of air pollution incidents in Morocco. Furthermore, the developed models exhibit versatility, enabling their application for estimating future trends of individual pollutants or making real-time predictions of air quality levels. This research contributes to advancing the understanding and proactive management of air quality in the context of Morocco, offering valuable insights for pollution control efforts.

Use of Leaves as Bio-Indicator to Assess Air Pollution Tolerance Index (APTI) And Elemental Concentration of Heavy Metals at Katsina Central Market Grinding Area, Katsina State-Nigeria

Use of Leaves as Bio-Indicator to Assess Air Pollution Tolerance Index (APTI) And Elemental Concentration of Heavy Metals at Katsina Central Market Grinding Area, Katsina State-Nigeria

Comprehensive Assessment of Air, Soil, and Water Pollution for Environmental Health in Al-Hai City, Iraq

Sustainability has evolved into a broad phrase that includes practically every aspect of life on the Earth, from local to global, and over different periods. Sustainable biological systems include healthy, long-lived forests and wetlands. This paper aimed to study and measure pollution indicators in the Al-Hai city center's air, soil, and water in Wasit province, located at Coordinates (32.1742 N° and 46.0433 E°). The data for Al-Hai city's ambient air pollutants (CO, NO, NO2, H2S, SO2, and CO2) that considered air pollution was recorded for the date (12-13/12/2021). The Maximum values for the concentration of carbon monoxide CO and sulfur dioxide SO2 were 1.019 ppm and 0.0321 ppm, respectively. The water and sediment samples were collected in December 2021, and all samples were analyzed in the laboratory. Heavy elements (Cd, Pb, As, and Hg) were measured in soil and water. The samples were collected and taken from two stations in the Gharraf River of Al-Hai city in Wasit province to investigate the potential for environmental damage caused by these components in this region. This study will be conducted at two stations (North and South of Al-Hai city center) beginning in December 2021. In the south station, all heavy elements showed exponential growth in water and sediment samples, but concentrations in the north station were low. The highest value recorded in this study was for the pb concentration in the soil samples taken from the main street, which was 31.9 ppm.

Ambient Air Pollution Exposures and Child Executive Function: A US Multicohort Study.

Executive function, which develops rapidly in childhood, enables problem-solving, focused attention, and planning. Animal models describe executive function decrements associated with ambient air pollution exposure, but epidemiologic studies are limited. We examined associations between early childhood air pollution exposure and school-aged executive function in 1235 children from three US pregnancy cohorts in the ECHO-PATHWAYS Consortium. We derived point-based residential exposures to ambient particulate matter ≤2.5 µm in aerodynamic diameter (PM 2.5 ), nitrogen dioxide (NO 2 ), and ozone (O 3 ) at ages 0-4 years from spatiotemporal models with a 2-week resolution. We assessed executive function across three domains, cognitive flexibility, working memory, and inhibitory control, using performance-based measures and calculated a composite score quantifying overall performance. We fitted linear regressions to assess air pollution and child executive function associations, adjusting for sociodemographic characteristics, maternal mental health, and health behaviors, and examined modification by child sex, maternal education, and neighborhood educational opportunity. In the overall sample, we found hypothesized inverse associations in crude but not adjusted models. Modified associations between NO 2 exposure and working memory by neighborhood education opportunity were present ( Pinteraction = 0.05), with inverse associations more pronounced in the "high" and "very high" categories. Associations of interest did not differ by child sex or maternal education. This work contributes to the evolving science regarding early-life environmental exposures and child development. There remains a need for continued exploration in future research endeavors, to elucidate the complex interplay between natural environment and social determinants influencing child neurodevelopment.

Validity of Mobility-Based Exposure Assessment of Air Pollution: A Comparative Analysis with Home-Based Exposure Assessment.

Air pollution exposure is typically assessed at the front door where people live in large-scale epidemiological studies, overlooking individuals' daily mobility out-of-home. However, there is limited evidence that incorporating mobility data into personal air pollution assessment improves exposure assessment compared to home-based assessments. This study aimed to compare the agreement between mobility-based and home-based assessments with personal exposure measurements. We measured repeatedly particulate matter (PM2.5) and black carbon (BC) using a sample of 41 older adults in the Netherlands. In total, 104 valid 24 h average personal measurements were collected. Home-based exposures were estimated by combining participants' home locations and temporal-adjusted air pollution maps. Mobility-based estimates of air pollution were computed based on smartphone-based tracking data, temporal-adjusted air pollution maps, indoor-outdoor penetration, and travel mode adjustment. Intraclass correlation coefficients (ICC) revealed that mobility-based estimates significantly improved agreement with personal measurements compared to home-based assessments. For PM2.5, agreement increased by 64% (ICC: 0.39-0.64), and for BC, it increased by 21% (ICC: 0.43-0.52). Our findings suggest that adjusting for indoor-outdoor pollutant ratios in mobility-based assessments can provide more valid estimates of air pollution than the commonly used home-based assessments, with no added value observed from travel mode adjustments.

Assessment of air pollution in South Kazakhstan using moss (Hylocomium splendens) biomonitoring technique and neutron activation analysis

Assessment of air pollution in South Kazakhstan using moss (Hylocomium splendens) biomonitoring technique and neutron activation analysis

Assessment of atmospheric air pollution in Dnipro city as a result of dust entrainment from the ash-disposal areasof Prydniprovsk TPP

Purpose. The purpose of this work is to assess the pollution of atmospheric air in Dnipro city as a result of dust entrainment from the ash-disposal areas of Prydniprovsk TPP. The methods. Assessment of atmospheric air pollution in the city of Dnipro as a result of dust entrainment from the ash-disposal areas of Prydniprovska TPP was carried out as follows: physical and chemical characteristics of the stored fly ash were determined; specific dust entrainment from the surface of dry beaches of ash-disposal areas under the most favorable conditions was determined; mathematical modeling of the spread of atmospheric air pollution of Dnipro city by suspended particles was carried out using UPCAP "ECO Center"program; a comparative assessment of dust entrainment reduction due to the gradual processing of the deposited fly ash was carried out. Findings. Almost 100% of the total mass of ash consists of particles smaller than 80 μm, the content of particles smaller than 10 and 2.5 μm is 27.7 and 8.2%, respectively. The main chemical compounds contained in ash are silicon dioxide, aluminum oxide, iron (III) oxide and carbon. The gross entrainment of substances in the form of suspended fine-grained solid particles from the surface of the dry beaches of the Prydniprovska TPP ash-disposal facilities is 18,654 tons per year. The area of atmospheric air pollution of Dnipro city by suspended particles is about 850 km2 and covers Samarskyi, Sobornyi, partly Industrialnyi and Shevchenkivskyi Districts. As a result of the implementation of a waste-free technology for the processing of fly ash with the utilization of the iron-containing fraction, the area of pollution can be reduced by 13.5 times up to 63 km2, the contamination zone in this case will cover underpopulated part of Samarskyi District of the city of Dnipro. Scientific novelty. A refined dependence of the particle size distribution of the mature Prydniprovsk TPP fly ash particles was determined for the further assessment of potential dust entrainment. For the first time, modeling of the distribution of fine-grained particles of fly ash of the Prydniprovsk TPP in the atmospheric air of the city of Dnipro was carried out, taking into account meteorological conditions, conditions of dust formation and particle size distribution. Practical implementation. The results of the research can contribute to reducing the level of atmospheric air pollution in the city of Dnipro. This will improve the quality of life of the local population and protect the natural environment from negative impacts. Research results can also be a basis for the development and implementation of technological solutions for the waste-free processing of stored fly ash with marketable products obtaining.

Comparative study of air quality index of two metropolitan cities (Lucknow and Kanpur) in Year-2023

In 2023, it was declared that the Bangladesh at the first place and then Pakistan, after which the most polluted country is India at the third place. Polluted air is becoming a very considerate issue all over the world. This study is all about comparing the air quality of two metro cities, Lucknow and Kanpur during (November 2022- October,2023) buy monitoring and assessment of few selective basic AQI air pollutants namely Particulate Matter (PM10) , Sulphur dioxide (SO2), Nitrogen dioxide (NO2) concentrations and also AQI of Lucknow and Kanpur at 5 representative locations categorized as residential, commercial and industrial areas. Residential area in Lucknow (Mahanagar and Aliganj) and in Kanpur (Kidwai Nagar and Shastri Nagar), Commercial areas in Lucknow (Hazarat Ganj and Ansal T.C.) and in Kanpur (Zareeb Chauki and Ramadevi), Industrial area Talkatora in Lucknow and Panki in Kanpur have been taken under consideration. The result found that 24 hours concentration of PM10 were in the range of 76.40 to 261.19 µg/m3 in Lucknow and 74.68 to 400.67 µg/m3 in Kanpur with an average of 174.34 and 160.36 µg/m3, respectively. The average value of PM10 was above as per the guideline of WHO and permissible limit prescribed by NAAQ in both the cities. Similarly, 24 hours average concentration of SO2 was below the prescribed level of NAAQ in Lucknow and Kanpur which was in the range of 5.33 to 12.70 µg/m3 in Lucknow and 2.00 to 9.53 µg/m3 in Kanpur with an average of 8.71 and 7.19 µg/m3, respectively. The 24 hours average concentration of Nitrogen dioxide (NO2) was below prescribed level of NAAQ in Lucknow and Kanpur. In Lucknow it was in the range of 13.83 to 48.01 µg/m3 and in Kanpur 21.45 to 64.02 µg/m3 with an average of 29.45 and 50.14 µg/m3, respectively. The 24 hours AQI was reported in the range of 76 to 211 in Lucknow and 75 to 363 in Kanpur with an average of 150.84 and 142.76 respectively which is greater moderate to severe by NAAQ and WHO guideline throughout the year. Ultimately, this entire study is related to human health because polluted air gives rise to various hazardous diseases such as asthma cancer, premature death cardiovascular diseases, respiratory tract diseases etc.

Association between rheumatic musculoskeletal diseases and air pollution: myth or reality?

This review discusses the impact of air pollution exposure on rheumatic musculoskeletal diseases, and also considers air pollution assessment and its limitations. Rheumatic diseases are caused by dysregulation and excessive activation of the immune system, leading to systemic inflammation and immune-mediated damage. Air pollution, particularly particulate matter originating from fossil fuel combustion, increases the production of inflammatory cytokines and activates pathways involved in the inflammatory response. Long-term and short-term exposure to air pollution can be evaluated using various methodologies. Despite the limitations of exposure assessment, the review highlights the importance of monitoring air pollution levels to mitigate their harmful effects on human health and the environment. KEY WORDS: Autoimmune diseases, rheumatic musculoskeletal diseases (RMDs), pollution, environment.

Monitoring and Forecasting the Spread of Industrial Emissions in the Atmosphere in Real Time SUSUPLUME

The "Ecomonitor" software package has been considered, suitable for use in various industrial companies for tracking and forecasting the industrial emissions into the atmosphere, taking into account meteorological conditions in order to determine the transfer of pollutants to the area around the company. The mathematical model SUSUPLUME, operating in "Ecomonitor", has been presented. The main advantages of this parametric model have been indicated, namely the ability to assess air pollution in real time, as well as its application to various substances, including SO2, NO2, NO, CO, CO2 and dust.

Atmospheric deposition studies of microplastics in Central Germany

AbstractEmission of microplastics (MP) to the atmosphere, airborne transport, and subsequent deposition are now recognized. However, the temporal and spatial resolution of data on MP pollution and knowledge of their atmospheric behaviour and fate is still very limited. Hence, we investigated MP wet and dry deposition in Central Germany and examined the role of weather conditions on MP contamination levels. Monthly samples of dry and wet deposition were taken over an eight-month period (05/2019-12/2019) and analysed by micro-Fourier-Transform Infrared spectroscopy (µFTIR) down to 11 μm particle size and one dry deposition sample was subjected to Raman analysis to determine plastic particles down to a size of 0.5 μm. MP in a size range from 11 μm to 130 μm were detected in all wet deposition samples and in 4 out of 8 dry deposition samples by µFTIR. Polypropylene particles were found most frequently and accounted for 62% and 54% of all particles in wet and dry deposition samples, respectively. Over the eight-month period, wet deposition of MP slightly dominated at the study site and comprised 59% of the total MP deposition. The MP mean total (wet + dry) deposition flux (DF) was 17 ± 14 MP m− 2 day− 1. Extensive Raman analyses of an exemplary dry deposition sample revealed additional plastic particles in the extended size range from 1 to 10 μm resulting in a deposition flux of 207 MP m− 2 day− 1. Our results suggest that MP analysis by µFTIR down to 11 μm may underestimate DF at least by an order of magnitude. More comprehensive studies on submicron plastics and nanoplastics are needed to fully assess air pollution by plastic particles.

Drone-Assisted Particulate Matter Measurement in Air Monitoring: A Patent Review

Air pollution is caused by the presence of polluting elements. Ozone (O3), carbon monoxide (CO), carbon dioxide (CO2), nitrogen dioxide (NO2), sulfur dioxide (SO2), and particulate matter (PM) are the most controlled gasses because they can be released into the atmosphere naturally or as a result of human activity, which affects air quality and causes disease and premature death in exposed people. Depending on the substance being measured, ambient air monitors have different types of air quality sensors. In recent years, there has been a growing interest in designing drones as mobile sensors for monitoring air pollution. Therefore, the objective of this paper is to provide a comprehensive patent review to gain insight into the proprietary technologies currently used in drones used to monitor outdoor air pollution. Patent searches were conducted using three different patent search engines: Google Patents, WIPO’s Patentscope, and the United States Patent and Trademark Office (USPTO). The analysis of each patent consists of extracting data that supply information regarding the type of drone, sensor, or equipment for measuring PM, the lack or presence of a cyclone separator, and the ability to process the turbulence generated by the drone’s propellers. A total of 1473 patent documents were retrieved using the search engine. However, only 13 met the inclusion criteria, including patent documents reporting drone designs for outdoor air pollution monitoring. Therefore, was found that most patents fall under class G01N (measurement; testing) according to the International Patents Classification, where the most common sensors and devices are infrared or visible light cameras, cleaning devices, and GPS tracking devices. The most common tasks performed by drones are air pollution monitoring, assessment, and control. These categories cover different aspects of the air pollution management cycle and are essential to effectively address this environmental problem.