Sources Of Air Pollution Research Articles

Implications of Exposure to Air Pollution on Male Reproduction: The Role of Oxidative Stress.

Air pollution, either from indoor (household) or outdoor (ambient) sources, occurs when there is presence of respirable particles in the form of chemical, physical, or biological agents that modify the natural features of the atmosphere or environment. Today, almost 2.4 billion people are exposed to hazardous levels of indoor pollution, while 99% of the global population breathes air pollutants that exceed the World Health Organization guideline limits. It is not surprising that air pollution is the world's leading environmental cause of diseases and contributes greatly to the global burden of diseases. Upon entry, air pollutants can cause an increase in reactive oxygen species (ROS) production by undergoing oxidation to generate quinones, which further act as oxidizing agents to yield more ROS. Excessive production of ROS can cause oxidative stress, induce lipid peroxidation, enhance the binding of polycyclic aromatic hydrocarbons (PAHs) to their receptors, or bind to PAH to cause DNA strand breaks. The continuous and prolonged exposure to air pollutants is associated with the development or exacerbation of pathologies such as acute or chronic respiratory and cardiovascular diseases, neurodegenerative and skin diseases, and even reduced fertility potential. Males and females contribute to infertility equally, and exposure to air pollutants can negatively affect reproduction. In this review, emphasis will be placed on the implications of exposure to air pollutants on male fertility potential, bringing to light its effects on semen parameters (basic and advanced) and male sexual health. This study will also touch on the clinical implications of air pollution on male reproduction while highlighting the role of oxidative stress.

Environmental Chemistry: A Study on Air Pollution

Environmental chemistry plays a crucial role in understanding the interactions between human activities and the environment. Air pollution, a significant concern within environmental chemistry, entails the presence of harmful substances in the atmosphere, adversely affecting ecosystems, human health, and climate. This study delves into the complexities of air pollution, examining its primary and secondary pollutants, and the natural and anthropogenic sources contributing to its prevalence. Primary pollutants, such as particulate matter (PM), sulfur dioxide (SO), nitrogen oxides (NO), carbon monoxide (CO), and volatile organic compounds (VOCs), directly enter the atmosphere from various sources. In contrast, secondary pollutants like ozone (O) and secondary particulate matter form through atmospheric reactions involving primary pollutants. Natural sources, including volcanic eruptions, wildfires, dust storms, biogenic emissions, and sea spray, contribute to background pollution levels. However, human activities, particularly industrial processes, transportation, agriculture, and residential practices, significantly amplify pollutant emissions. This paper focuses on air pollution, a critical aspect of environmental chemistry, examining its sources, types of pollutants, and their effects. The study highlights natural and anthropogenic sources of air pollution, emphasizing the importance of emission factors in understanding pollution levels. Through an extensive review of existing literature and data, this paper aims to provide a comprehensive understanding of air pollution and its implications for environmental and public health.

Temporal assessment of emission inventory model for Indian heavy commercial vehicle segment – A top-down approach

Heavy commercial vehicles (HCVs) are pivotal to India's economy but are also significant sources of air pollution. To address this issue, the Indian government implemented Bharat Stage VI (BS-VI) emission standards in 2020. Research gap regarding realistic inputs for annual vehicle kilometres and survival rate of heavy commercial vehicles has been identified. The HCV sector is categorized into long-haul vehicles (32 tonnes and above, with higher annual vehicle usage and survival rates) and pick-up and delivery HCV trucks (16–32 tonnes, with relatively lower annual vehicle usage and survival rates). Based on the primary research by taking into account the inputs from various stakeholders regarding annual vehicle kilometres and survival rates subject to vehicle type and emission standards, an HCV emission inventory for India has been developed for regulated pollutants (NOx, PM2.5, and CO) till 2035. We assume no additional external technological or policy interventions, except the anticipated shift to Bharat Stage VII (BS-VII) standards by 2027. Key findings reveal that the on-road HCV population is projected to marginally increase (6.5%) by 2035 compared to 2020. However, there is a notable 97% surge in goods transport tonnage by 2035, indicating more efficient commercial vehicle usage, especially in the heavier category (32 tonnes and above). Crucially, annual emissions of NOx, PM2.5, and CO from the HCV segment are expected to peak in 2020 and decline significantly by 2035. Emissions are projected to decrease by 91.5% (NOx), 96.6% (PM2.5), and 97.6% (CO) compared to 2020 levels due to the introduction of BS-VI standards in 2020 and the anticipated adoption of BS-VII standards in 2027. This study is instrumental in defining base emission inventory till 2035 for any further policy evaluation for heavy commercial vehicle segment for reducing air pollution and enhancing environmental sustainability.

ОТКРЫТЫЙ ВОПРОС ЭКОЛОГИЧЕСКОГО УЩЕРБА

The article touches on the issue of environmental damage and names the main sources of air pollution. Atmospheric pollution is difficult to assess; all calculations determine conditional damage, and this component of the natural environment is a vital element of the existence of humanity itself. Therefore, the degree of industrialization with its anthropogenic interventions in the natural environment began to threaten the parity of the “nature – technology – man” model. An aspect of the domestic regulatory framework of environmental law is considered: the concepts of “pollutant”, “standards of permissible emissions”, “stationary source of environmental pollution” are given - and the interpretation of the categories “ecological damage to the natural environment”, “damage from the effects of atmospheric pollution on the state of the environment and the economy of the regions, as well as individual natural resource users”, “prevented environmental damage from emissions of pollutants into the atmosphere”, “reduced mass of pollutants”. The federal project “Clean Air” of the national project “Ecology” was considered, which will directly update the situation on the open issue of environmental damage in terms of pollution by harmful components of atmospheric air. Also, one of the main components of the openness of the issue: firstly, in theoretical terms, the absence of clear concepts in federal legislation; secondly, insufficient systematization of primary data at the regional level; and thirdly, the difficulty of accurate assessment.

Portable Air Cleaner Usage and Particulate Matter Exposure Reduction in an Environmental Justice Community: A Pilot Study.

Particulate matter (PM) exposure is associated with adverse health outcomes, including respiratory illness. A large fraction of exposure to airborne contaminants occurs in the home. This study, conducted over 5 months in a community with high asthma rates (Chelsea, MA, USA), investigated the use of portable air cleaners (PACs) to reduce indoor PM. Seven asthma-affected households participated, receiving a PAC (Austin Air Health Mate HEPA filter), a QuantAQ sensor to measure PM1, PM2.5, PM10 (µg/m3), and a HOBO plug-load data logger to track PAC usage. Results describe hourly and daily PM concentrations and PAC usage for each household. Hourly average PM concentrations decreased when PACs were turned on (vs. when they were turned off) across households during the study period: PM1 decreased by 0.46 µg/m3, PM2.5 decreased by 0.69 µg/m3, and PM10 decreased by 3.22 µg/m3. PAC usage varied for each household, including constant usage in one household and only usage at certain times of day in others. Higher filtration settings led to lower PM, with significant reductions in some, but not all, homes. Our findings highlight some difficulties in implementing household PAC interventions, yet also provide evidence to support household-level interventions to reduce PM and other indoor sources of air pollution. We also highlight academic-community partnerships as contributing to evidence-based solutions.

Air Pollution Caused by Crop Waste and its Effects on Human Health

Agriculture, and more specifically the combustion of crop waste, is now widely acknowledged to be a global environmental problem. Because of this, the issue was not initially brought to light. This literature review investigates crop residues as a potential source of air pollution and the associated health risks. This review will also investigate the factors that contribute to the contamination of water by crop residue. This article will discuss the pollutants that are emitted during the combustion of crop waste, how they affect the quality of the air we breathe, and how they affect the health of humans. This article provides a concise summary of the pollutants caused by the burning of crop waste. This analysis also investigates the effects that crop waste burning has on human health and the natural environment, as well as potential policy solutions and preventative measures.

What Makes the Indian Megacity Chennai’s Air Unhealthy? - A Bottom-up Approach to Understand the Sources of Air Pollutants

What Makes the Indian Megacity Chennai’s Air Unhealthy? - A Bottom-up Approach to Understand the Sources of Air Pollutants

Health impact assessment of port-sourced air pollution in Barcelona.

Air pollution is a major health risk factor. Ports might be an understudied source of air pollution. We conducted a spatial health impact assessment (HIA) of port-sourced air pollution for Barcelona for 2017 at the neighbourhood level. Total NO2 and PM10 and port-sourced NO2, PM10 and PM2.5 concentrations were available through the ADMS-Urban model. Population data, mortality and morbidity data, and risk estimates were obtained. We followed standard HIA methodologies and calculated relative risks and impact fractions for 1.35 million adults living in 73 neighbourhoods. The city-wide mean total NO2 and PM10 concentrations were 37.88 μg/m3 (range: 19.61-52.17 μg/m3) and 21.68 μg/m3 (range: 17.33-26.69 μg/m3), respectively, of which 7% (range: 2-36%) and 1% (range: 0-7%) were port-sourced, respectively. The mean port-sourced PM2.5 concentration was 0.19 μg/m3 (range: 0.06-1.38 μg/m3). We estimated that 1,123 (PI: 0-3,060) and 1,230 (95% CI: 0-2,566) premature deaths were attributable to total NO2 and PM10, respectively, of which 8.1% (91; PI: 0-264) and 1.1% (13; 95% CI 0-29) were attributable to port-sourced NO2 and PM10, respectively. 20 (95% CI: 15-26) premature deaths were attributable to port-sourced PM2.5. Additionally, a considerable morbidity burden and losses in life expectancy were attributable to port-sourced air pollution. Neighbourhoods closest to the port in the south-east were most adversely affected, gradually decreasing towards the north-west. The port is an understudied air pollution source in Barcelona with strong health impacts. Cities need local insight into health risk factors, their sources, attributable burdens and distributions for defining targeted policies.

What is Air Pollution Source Apportionment: An illustrated Primer

What is Air Pollution Source Apportionment: An illustrated Primer

Research on multi-UAV air pollution source localization algorithm based on emotional quantity

With the advancement of industrialization, the problem of atmospheric environmental pollution is becoming more and more prominent. To solve this problem, an unmanned aerial vehicle (UAV) as an airborne platform was used to design an air pollution source localization method based on an anxiety-auction algorithm and verify the feasibility of the algorithm through simulation analysis and indoor source localization experiments. The algorithm innovatively introduces the concept of anxiety in psychology into the traditional auction algorithm. By enabling each drone to make a “rational” auction time decision based on its emotional state, team resources can be conserved, and overall source localization efficiency can be enhanced. Based on different environmental factors and conditions, the number of drones and other multi-perspective comparison analyses with the traditional auction algorithm, the analysis results show that the anxiety-auction algorithm performs better in terms of success rate and distance ratio. This paper also built a set of atmospheric pollutant source localization platforms, consisting of an ultra-wideband (UWB) indoor positioning device, UAV platform, source localization monitoring and control module, and the indoor source localization experiment of atmospheric pollutants based on multiple UAVs was successfully designed and carried out.

Utilizing Machine Learning-based Classification Models for Tracking Air Pollution Sources: A Case Study in Korea

Utilizing Machine Learning-based Classification Models for Tracking Air Pollution Sources: A Case Study in Korea

Spatial patterns of PM2.5 air pollution in Jakarta: Insights from mobile monitoring

A mobile air quality monitoring study in Jakarta was undertaken in 2022 by the Clean Air Catalyst consortium, in partnership with Google Earth Outreach and Aclima, taking air pollution measurements using four specially equipped Google Street View cars. This study aims to map street-level PM2.5 pollution in a set of neighborhoods (polygons) throughout Jakarta to evaluate the variability caused by local emission sources and to observe the exposure to air pollution in areas with different income levels. To address these aims, we monitored the concentration five days a week, 8 am to 5 pm, from September to December in eight polygons, stratified to include high and low-income areas. The cars covered a distance of 7,750 km and at least 22 visits per location. PM2.5 concentrations were measured at 1-second intervals using a light-scattering sensor. This high-resolution data collection allowed for detailed monitoring and analysis of concentrations in the study area. The measurements showed temporal variability consistent with meteorological and activity patterns in Jakarta, with higher measured concentrations in the morning compared to the afternoon at most polygons. Median concentrations varied by 2x - >5x within individual polygons, and this variability was particularly evident in the northern part of Jakarta, encompassing both high-income and low-income neighborhoods, where these areas are significantly affected by traffic and industrial activities. The results demonstrate the extent to which PM2.5 concentrations and exposures within Jakarta neighborhoods depend on proximity to local air pollution sources.

Investigating Indoor Air Pollution Sources and Student’s Exposure Within School Classrooms: Using a Low‐Cost Sensor and Source Apportionment Approach

Indoor air quality is becoming one of the most important factors for public health, as people tend to spend more time indoors, either at home or at work. This paper combines the use of calibrated low‐cost particulate matter (PM) sensors with source apportionment algorithms to understand the factors that affect the indoor air quality within a typical UK school. Low‐cost optical particle counter (OPC) sensors were placed in three different classrooms within the school, measuring PM concentrations during a typical school week and the subsequent holiday period, to understand the role of occupancy within schools for air quality. When students were in attendance during school hours (09:00–15:30), the classroom with the greatest average PM levels had PM2.5 and PM10 mass concentrations of 14.4 and 51.0 μg/m3, respectively. During school hours, when students were present, all classrooms had average PM2.5 concentrations greater than 8.1 μg m−3 and average PM10 concentrations greater than 13.1 μg m−3. Notably, the English studies classroom exceeded the 24‐h WHO ambient PM10 guideline (45 μg m−3). Employing the non‐negative matrix factorization (NMF) algorithm for source apportionment revealed that between 93% and 98% of PM1 observed within classrooms derived from outdoor sources. This contribution diminished as particle size increased, with outdoor sources accounting for 74%–89% of PM2.5, and 19%–40% of PM10, respectively. The differences in classroom PM concentration and source percentages are attributed to differences in lesson activities, lesson frequency, flooring (carpeted versus hard flooring), location within the school, and proximity to outdoor sources such as roads. The approach described within the paper is easily translated to other indoor locations and could also be straightforwardly scaled due to its relatively low cost. Thereby, it allows for air quality management in locations crucial for the public health and educational outcomes of children.

Role of an Ultra-Large Coal-Fired Power Plant in PM2.5 Pollution in Taiwan

Taichung Power Plant (TPP) is Taiwan’s largest coal-fired power plant and is considered a major source of air pollution. During periods of deteriorating air quality, it is often required to reduce the load to reduce emissions. However, frequent power load shedding not only requires cost but also requires safety considerations. Therefore, it is necessary to explore the role that thermal power plant emissions play in air pollution in Taiwan. This study employed the Community Multiscale Air Quality modeling system with the brute-force method to analyze the PM2.5 concentration contributed by TPP. The results showed that among the various air basins in Taiwan, the Yun-Chi-Nan air basin (YCNAB), located to the south of TPP, was most severely affected by TPP’s emissions, with an annual average affected concentration of 1.0 µg m−3 (3.3%). However, when serious PM2.5 pollution events (daily concentration > 70 µg/m3) occurred due to low wind speeds, the Central Taiwan air basin (CTAB), where TPP is located, became the area most severely affected by TPP’s emissions. The low wind speed was caused by the interaction between the easterly wind field around Taiwan and Taiwan’s north–south mountain ranges. When this happens, TPP’s emissions would have a greater impact on the PM2.5 concentration at nearby stations in the CTAB and YCNAB, up to about 11%. Overall, on pollution days caused by low wind speeds, the largest TPP load reduction (40%) still had a certain effect as an emergency measure to improve the high PM2.5 pollution in central and southern Taiwan.

Air Pollution Assessment in Turhal District: Temporal Analysis of Pollutants

Since the last century, the rapidly increasing global population, along with industrialization and urbanization, has increased the type and amount of pollutants in the atmosphere. Particularly, the dependency on fossil fuels for energy production, transportation, and industrial activities has become one of the main sources of air pollution. This has led to an air pollution problem that negatively affects the quality of life in urban areas. However, in this study conducted in the Turhal district of Tokat, an optimistic development has been observed in recent years. Based on the data obtained from the air quality monitoring stations located in the region, the concentrations of pollutants such as PM10, NO2, NOX, and SO2 have been thoroughly examined. The analysis results indicate a significant decreasing trend in the concentrations of these pollutants from 2020 to 2023. This positive trend can be attributed to the increasing awareness of environmental protection, technological innovations, and societal consciousness. The study demonstrates the positive impacts on regional air quality during this period.

Chemometric Techniques in the Assessment of Ambient Air Quality and Development of Air Quality Index of Coal Mining Complex: A Statistical Approach

This study aims to analyze the regional variation in the source of air pollution, identify the percentage contribution of each pollutant, and distribute the mass contribution of each source category using multivariate analysis. The nine air monitoring sites were successfully divided into three groups using hierarchical agglomerative cluster analysis (HACA) (clusters 1, 2, and 3). The collected meteorological data is non-parametric data for the years 2020–2021 which includes PM2.5, PM10, SO2, NO2, NO, NOx, CO, wind speed, humidity, wind direction, temperature, cloud cover, and surface radiation. The most major air pollution sources were identified using FA. Multiple linear regression (MLR) and principal component regression (PCR) were utilized to create an equation model explaining contaminants' impact in each cluster. However, it was shown that the most important pollutants impacting the value of the air pollutant index (API) are gaseous pollutants (NOx and SO2) and particulate matter (PM10 and PM2.5). Gas and non-gas pollutants have a 65% influence on cluster 1 and meteorological conditions have a 35% effect. Cluster 3 is influenced by 65% particle and non-gas pollutants and 35% weather conditions, compared to Cluster 2 which is 100% affected by gas and particulate pollutants because of its spatial location. This study shows the value of the multivariate modeling technique in minimizing the time and expense associated with monitoring redundant stations and parameters.

Wildfire and African dust aerosol oxidative potential, exposure and dose in the human respiratory tract

Exposure to wildfire smoke and dust can severely affect air quality and health. Although particulate matter (PM) levels and exposure are well-established metrics linking to health outcomes, they do not consider differences in particle toxicity or deposition location in the respiratory tract (RT). Usage of the oxidative potential (OP) exposure may further shape our understanding on how different pollution events impact health. Towards this goal, we estimate the aerosol deposition rates, OP and resulting OP deposition rates in the RT for a typical adult Caucasian male residing in Athens, Greece. We focus on a period when African dust (1–3 of August 2021) and severe wildfires at the northern part of the Attika peninsula and the Evia island, Greece (4–18 of August 2021) affected air quality in Athens. During these periods, the aerosol levels increased twofold leading to exceedances of the World Health Organization (WHO) [15(5) μg m−3] PM10 (PM2.5) air quality standard by almost 100 %. We show that the OP exposure is 1.5-times larger during the wildfire smoke events than during the dust intrusion, even if the latter was present in higher mass loads – because wildfire smoke has a higher specific OP than dust. This result carries two important implications: OP exposure should be synergistically used with other metrics - such as PM levels - to efficiently link aerosol exposure with the resulting health effects, and, certain sources of air pollution (in our case, exposure to biomass burning smoke) may need to be preferentially controlled, whenever possible, owing to their disproportionate contribution to OP exposure and ability to penetrate deeper into the human RT.

Chemometric Techniques in the Assessment of Ambient Air Quality and Development of Air Quality Index of Coal Mining Complex: A Statistical Approach

This study aims to analyze the regional variation in the source of air pollution, identify the percentage contribution of each pollutant, and distribute the mass contribution of each source category using multivariate analysis. The nine air monitoring sites were successfully divided into three groups using hierarchical agglomerative cluster analysis (HACA) (clusters 1, 2, and 3). The collected meteorological data is non-parametric data for the years 2020–2021 which includes PM2.5, PM10, SO2, NO2, NO, NOx, CO, wind speed, humidity, wind direction, temperature, cloud cover, and surface radiation. The most major air pollution sources were identified using FA. Multiple linear regression (MLR) and principal component regression (PCR) were utilized to create an equation model explaining contaminants' impact in each cluster. However, it was shown that the most important pollutants impacting the value of the air pollutant index (API) are gaseous pollutants (NOx and SO2) and particulate matter (PM10 and PM2.5). Gas and non-gas pollutants have a 65% influence on cluster 1 and meteorological conditions have a 35% effect. Cluster 3 is influenced by 65% particle and non-gas pollutants and 35% weather conditions, compared to Cluster 2 which is 100% affected by gas and particulate pollutants because of its spatial location. This study shows the value of the multivariate modeling technique in minimizing the time and expense associated with monitoring redundant stations and parameters.

Chemometric Techniques in the Assessment of Ambient Air Quality and Development of Air Quality Index of Coal Mining Complex: A Statistical Approach

This study aims to analyze the regional variation in the source of air pollution, identify the percentage contribution of each pollutant, and distribute the mass contribution of each source category using multivariate analysis. The nine air monitoring sites were successfully divided into three groups using hierarchical agglomerative cluster analysis (HACA) (clusters 1, 2, and 3). The collected meteorological data is non-parametric data for the years 2020–2021 which includes PM2.5, PM10, SO2, NO2, NO, NOx, CO, wind speed, humidity, wind direction, temperature, cloud cover, and surface radiation. The most major air pollution sources were identified using FA. Multiple linear regression (MLR) and principal component regression (PCR) were utilized to create an equation model explaining contaminants' impact in each cluster. However, it was shown that the most important pollutants impacting the value of the air pollutant index (API) are gaseous pollutants (NOx and SO2) and particulate matter (PM10 and PM2.5). Gas and non-gas pollutants have a 65% influence on cluster 1 and meteorological conditions have a 35% effect. Cluster 3 is influenced by 65% particle and non-gas pollutants and 35% weather conditions, compared to Cluster 2 which is 100% affected by gas and particulate pollutants because of its spatial location. This study shows the value of the multivariate modeling technique in minimizing the time and expense associated with monitoring redundant stations and parameters.

Premature deaths from fine particles PM2.5 air pollution in regional capitals of Slovakia during 2016-2020 period.

The purpose of this quantitative study is to assess the impact of fine particles air pollution in major cities of Slovakia. The study aims to estimate number of premature deaths from long-term exposure to fine particles PM2.5 in eight regional capitals of Slovakia in the period 2016-2020. Consequently, the study aims to conduct a comparative analysis using secondary derived indicators. For calculations of estimated premature deaths from long-term exposure to fine particles PM2.5 air pollution we used standardized methodology developed by the World Health Organization and the European Environment Agency. The annual average of estimated premature deaths from PM2.5 air pollution in the studied period was in Bratislava 353, Košice 219, Prešov 84, Žilina 90, Banská Bystrica 76, Nitra 73, Trnava 59, and Trenčín 52. In relative terms per 1,000 inhabitants Bratislava had annual average 1.14 of estimated premature deaths, Košice 1.32, Prešov 1.38, Žilina 1.61, Banská Bystrica 1.35, Nitra 1.35, Trnava 1.27, and Trenčín 1.31. Bratislava as the largest city in Slovakia recorded the smallest relative number of estimated premature deaths. The worst results were recorded by the city of Žilina. The estimated number of premature deaths from long-term exposure to particulate matter air pollution in the regional capitals decreased in the given period. The most of the regional capitals with the exception of Bratislava and Žilina, showed similar levels of estimated premature deaths. However, the current geopolitical situation and rising energy prices threaten return to solid fuel burning which is the largest source of particulate matter air pollution in Slovakia and thus reversing positive trends.