National Ambient Air Quality Standards Research Articles

Insight Study of Trace Elements in PM2.5 During Nine Years in Delhi, India: Seasonal Variation, Source Apportionment, and Health Risks Assessment.

This study investigated the concentrations, seasonal variations, sources, and human health risks associated with exposure to heavy elements (As, Al, Pb, Cr, Mn, Cu, Zn, and Ni) of PM2.5 at an urban location of Delhi (28° 38' N, 77° 10' E; 218m amsl), India, from January 2013 to December 2021. The average mass concentration of PM2.5 throughout the study period was estimated as 127 ± 77µgm-3, which is exceeding the National Ambient Air Quality Standards (NAAQS) limit (annual: 40µgm-3; 24h: 60µgm-3). The seasonal mass concentrations of PM2.5 exhibited at the order of post-monsoon (192 ± 110 µgm-3) > winter (158 ± 70 µgm-3) > summer (92 ± 44 µgm-3) and> monsoon (67 ± 32 µgm-3). The heavy elements, Al (1.19µgm-3), Zn (0.49µgm-3), Pb (0.43µgm-3), Cr (0.21µgm-3), Cu (0.21µgm-3), Mn (0.07µgm-3), and Ni (0.14µgm-3) exhibited varying concentrations in PM2.5, with the highest levels observed in the post-monsoon season, followed by winter, summer, and monsoon seasons. Six primary sources throughout the study period, contributing to PM2.5 were identified by positive matrix factorization (PMF), such as dust (paved/crustal/soil dust: 29.9%), vehicular emissions (17.2%), biomass burning (15.4%), combustion (14%), industrial emissions (14.2%), and Br-rich sources (9.2%). Health risk assessments, including hazard quotient(HQ), hazard index(HI), and carcinogenic risk(CR), were computed based on heavy elements concentrations in PM2.5. Elevated HQvalues for Cr and Mn linked with adverse health impacts in both adults and children. High carcinogenic risk values were observed for Cr in both adults and children during the winter and post-monsoon seasons, as well as in adults during the summer and monsoon seasons. The combined HIvalue exceeding one suggests appreciable non-carcinogenic risks associated with the examined elements. The findings of this study provide valuable insights into the behaviour and risk mitigation of heavy elements in PM2.5, contributing to the understanding of air quality and public health in the urban environment of Delhi.

Model misspecification, measurement error, and apparent supralinearity in the concentration-response relationship between PM2.5 and mortality.

A growing number of studies have produced results that suggest the shape of the concentration-response (C-R) relationship between PM2.5 exposure and mortality is "supralinear" such that incremental risk is higher at the lowest exposure levels than at the highest exposure levels. If the C-R function is in fact supralinear, then there may be significant health benefits associated with reductions in PM2.5 below the current US National Ambient Air Quality Standards (NAAQS), as each incremental tightening of the PM2.5 NAAQS would be expected to produce ever-greater reductions in mortality risk. In this paper we undertake a series of tests with simulated cohort data to examine whether there are alternative explanations for apparent supralinearity in PM2.5 C-R functions. Our results show that a linear C-R function for PM2.5 can falsely appear to be supralinear in a statistical estimation process for a variety of reasons, such as spatial variation in the composition of total PM2.5 mass, the presence of confounders that are correlated with PM2.5 exposure, and some types of measurement error in estimates of PM2.5 exposure. To the best of our knowledge, this is the first simulation-based study to examine alternative explanations for apparent supralinearity in C-R functions.

A Quantitative Assessment of Air Particulate Matter Concentration and Its Correlation with the Climatic Factor of Dinajpur City, Bangladesh

Particulate matter (PM) is the air pollutant that poses the most significant risk to human health compared to other measured criteria pollutants. One of our main goals was to assess the quantities of particulate matter (0.3-10µm) in the Dinajpur municipality's sensitive zones, roads, and residential and commercial buildings. Light scattering particle counters, the Extech VPC-300, were used to measure particulate matter and climatic data at 26 distinct locations in Dinajpur. R 4.2.2 and the data analysis tool in Microsoft Excel were utilized for the statistical analysis. The highest values for PM 0.3, 0.5, 1, 2.5, 5, and 10 µm are 335195, 138118, 37965, 12995, 2087, and 2507 and medium value is accordingly 221304, 71873, 16681, 3393, 361 and 454, respectively. The PM 0.5 μm particle range, which includes 12 samples, is highly concerned and getting closer to the hazardous red polluted zone. Most places of PM 2.5 µm exceed the 2000 particles, which are hazardous for people. The link between climatic variables and particulate matter (PM 0.5-10 µm) significantly correlated with temperature. Humidity and PM (0.3-10 µm) measurements in the study area show a negative correlation. According to observations, factors contributing to the high PM concentration include excessive human movement, combustion byproducts of black smoke, road dust, waste, garbage in every possible place, and rice mill industry emissions.

An evaluation of air quality impact prediction performance undertaken as part of environmental impact assessment (EIA) in India

Effective implementation of the Environmental Impact Assessment (EIA) is recognised as a global issue, in particular the impact prediction stage, which is the ‘core’ of EIA. Consisting of four stages: impact identification, impact assessment, significance evaluation, and mitigation measures on the possible environmental repercussions of project developmental activities, the efficacy of impact prediction can define the quality of the EIA process, which will better align environmental decision-making to sustainable development. The weakness of impact prediction in EIA demands more study to enhance practice. Although this is widely explored in the context of developed countries such as the UK, it is particularly concerning in India. A specialised review package built from several sources is utilised to assess the efficacy of air quality impact prediction, based on Lee & Colley (1991). 20 EIA reports of Category A (mega-scale projects causing significant environmental impacts) are reviewed. This study's evaluation indicates that significance evaluation and mitigation actions are the weakest phases and a major concern while assessing air quality studies conducted as a part of EIA. Recommendations to improve the process include prioritising the cumulative impact assessment within the regulatory framework, enhancing capacity building, embedding public participation and instilling accountability among stakeholders, which can be adopted globally. Additional recommendations specifically for India are revising the National Ambient Air Quality Standards (NAAQS), restructuring the EIA review mechanism by EAC and improving mitigation measures by adopting GIS and remote sensing technologies.

Ambient Air Quality Assessment through Air Quality Index and Air Quality Health Index for Eastern Coastal Region, Gandhidham, Gujarat, India

The impact of coastal developmental projects such as ports on regional air quality has been a matter of concern, as they are known to induce rapid industrialization and urbanization as collateral development on a long-term basis. Gandhidham-Kachchh, Gujarat, India is hub for industrialization, thus various developmental activities and port activities may release toxic gases into the environment. Thus, an attempt was made to assess the present ambient air quality in and around eastern coastal region of Gandhidham, Gujarat using Air Quality Index (AQI) and Air Quality Health Index (AQHI). The primary focus was on respirable particulate matter (PM10) and fine particulate matter (PM2.5) along with other gaseous pollutants. In total 134 samples were collected from 11 stations during January to December 2018. Sampling was carried out 24 hourly on a monthly basis. Respirable particulate matter (PM10), fine particulate matter (PM2.5), sulphur dioxide and nitrogen dioxide ranged from 29-401 µg/m3, 13-184 µg/m3, 5.9-92.6 µg/m3and 3.7-33 µg/m3, respectively. The annual mean concentrations varied from 118-227 µg/m3, 47-82 µg/m3, 10-51 µg/m3, 11-41 µg/m3 for PM10, PM2.5, sulphur dioxide and nitrogen dioxide, respectively. In most of the sampling locations both the particulate matter fractions exceeded the permissible limit as prescribed in National Ambient Air Quality Standard (NAAQS). The AQI ranged from 28 to 106, which falls under Good and Satisfactory category of pollution as per the Central Pollution Control Board (CPCB) standards except at A-11 which falls under Moderate category. The AQHI was in the range of 5.9 to 15.8, which was alarming indicating Moderate to Very Higher Risk to the people. The present findings throw light on the prevailing scenario in the ambient air quality of the studied region, thereby suggesting for strict implementation and thorough control measures for reduction of particulate matters in the air.

Total Suspended Particulate Matter (TSP)-Bound Carbonaceous Components in a Roadside Area in Eastern Indonesia

To evaluate carbonaceous components in the ambient air in the eastern region of Indonesia, 35 Total Suspended Particulate Matter (TSP) samples were collected on four characteristic roadsides on Sultan Alauddin Street, in Makassar City, using a high-volume air sampler. The average TSP concentration was 279.7 μg/m3, which exceeded both the National Ambient Air Quality Standard (NAAQS) of Indonesia and the World Health Organization (WHO) standards. The highest concentration reached 838.6 μg/m3 in the GR (gravel) site, which had the highest number of vehicles and was near a U-turn. TSP concentration was higher during peak hours (morning and late afternoon) than off-peak hours (noon). The main component of the total carbon (TC) fraction was organic carbon (OC), which showed a strong correlation with elemental carbon (EC) (r values for the morning, noon, and late afternoon were 0.89, 0.87, and 0.97, respectively), indicating that the carbon components were derived from common sources. TSP had a strong correlation with carbon components, except for char-EC. OC vs. soot-EC and EC vs. soot-EC also correlated well, suggesting the dominant influence of vehicle exhaust emissions. Non-exhaust emissions had a slight influence during peak hours, particularly at the GR site.

Inconsequential role for chemerin-like receptor 1 in the manifestation of ozone-induced lung pathophysiology in male mice.

We executed this study to determine if chemerin-like receptor 1 (CMKLR1), a Gi/o protein-coupled receptor expressed by leukocytes and non-leukocytes, contributes to the development of phenotypic features of non-atopic asthma, including airway hyperresponsiveness (AHR) to acetyl-β-methylcholine chloride, lung hyperpermeability, airway epithelial cell desquamation, and lung inflammation. Accordingly, we quantified sequelae of non-atopic asthma in wild-type mice and mice incapable of expressing CMKLR1 (CMKLR1-deficient mice) following cessation of acute inhalation exposure to either filtered room air (air) or ozone (O3), a criteria pollutant and non-atopic asthma stimulus. Following exposure to air, lung elastic recoil and airway responsiveness were greater while the quantity of adiponectin, a multi-functional adipocytokine, in bronchoalveolar lavage (BAL) fluid was lower in CMKLR1-deficient as compared to wild-type mice. Regardless of genotype, exposure to O3 caused AHR, lung hyperpermeability, airway epithelial cell desquamation, and lung inflammation. Nevertheless, except for minimal genotype-related effects on lung hyperpermeability and BAL adiponectin, we observed no other genotype-related differences following O3 exposure. In summary, we demonstrate that CMKLR1 limits the severity of innate airway responsiveness and lung elastic recoil but has a nominal effect on lung pathophysiology induced by acute exposure to O3.

Criteria pollutant and greenhouse gas emissions from cargo handling equipment operating at the Ports of Los Angeles and Long Beach

This study reports in-use emissions from eight pieces of diesel or natural gas cargo handling equipment (CHE) moving containerized freight at the Ports of Los Angeles and Long Beach. Equipment had engines certified to the legacy on-road model year (MY) 2010 or the off-road Tier 3, Tier 4 Interim, or Tier 4 Final emission standards. Overall, load factors were about half of the values in the California Air Resources Board's (CARB) current CHE emissions inventory, oxides of nitrogen (NOx) emissions were 2.7 times higher than certification standards, and tailpipe-emitted fine particulate matter (PM2.5) emissions were 2.2 times higher than certification standards. This is yet another study, the first dedicated to in-use operations by CHE at a large commercial seaport, showing elevated in-use emissions from combustion-powered mobile sources compared to certification levels. These results underscore the need to perform routine surveillance emissions testing of any off-road mobile source fleet when developing emission inventories and air quality programs for any jurisdiction worldwide. The Energy Economy Ratio (EER) – or ratio of increased efficiency from converting combustion to zero-emission battery-electric equipment – ranged from 2.8 to 3.7, which highlights potential energy savings and therefore greenhouse gas benefits of transitioning CHE and other freight sectors to zero-emission technologies.

A spatial approach to assessing PM2.5 exposure level of a brickmaking community in South Africa

ABSTRACT Globally, particulate matter with an aerodynamic diameter of 2.5 µm or less poses a significant threat to human health. The first step in quantifying human health impacts caused by exposure to PM2.5 pollution is exposure assessment. Population-weighted exposure level (PWEL) estimation is one of the methods that provides a more precise exposure assessment since it incorporates the spatiotemporal distribution of population with the pollution concentration estimate. In this study, PM2.5 exposure levels in the local communities around brickmaking industries were investigated, using the population census data of the study area and 1-year data from nine PM2.5 monitoring stations installed in and around the brickmaking industries. The observed PM2.5 data was spatially interpolated using inverse distance weight (IDW). Data on PM2.5 levels across the study area were classified based on the World Health Organization interim target (IT) guidelines and the South African National ambient air quality standard (NAAQS). An annual PM2.5 population weighted exposure level of 27.6 µg/m3 was estimated for the study area. However, seasonal exposure levels of 28.9, 37.6, 26.5, and 20.7 µg/m3 were estimated for the autumn, winter, spring, and summer seasons, respectively. This implies that local communities around the brick kiln in the Vhembe District are exposed to high levels of PM2.5, especially in winter. The PM2.5 levels in the brickmaking industries as well as its other sources in the Vhembe District, therefore, need to be lowered. Findings from population exposure level to pollutants can provide valuable data for formulating policies and recommendations on exposure reduction and public health protection. Implications: PM2.5 concentration in any given environment has high spatial and temporal variability due to the presence of diffused sources in the environment. Using ambient air concentrations to directly estimate population exposure without taking into consideration the disproportionate spatial and temporal distribution of the pollutant and the population may not yield accurate results on human exposure levels. It is, therefore, important to assess the aggregated PM2.5 exposure of a populace within a given area. This study therefore examines the PM2.5 population-weighted-exposure level of the host communities of the brickmaking industry in Vhembe District, South Africa.

Probabilistic Transport-Induced Emissions and Health Risks for Adelaide, South Australia

AbstractThe research aims to assess the risks associated with the transport-induced emissions (major criteria pollutants) in Adelaide, South Australia. A stochastic emission modelling strategy was implemented for a probabilistic risk assessment of the transport system for current and future scenarios, by analysing the road specific transport system data. The simulated result suggests that NOx and PM2.5 risks are significant at about 35% of city areas, especially along the roads with heavy vehicle concentration and higher traffic. The risk will significantly increase in 10 years if the current practice persists, demonstrating a high probability (more than 60%) of emissions above 150% of the air quality standard. The research suggests that appropriate planning is necessary to cope with the development needs for the system’s future sustainability.

Changes in source specific PM2.5 from 2010 to 2019 in New York and New Jersey identified by dispersion normalized PMF

Since the Clean Air Act amendments of 1970, various efforts have been made in the United States to control ambient particulate matter and improve air quality. Although substantial progress had been made by the end of the 1990s, further reductions were needed to meet the National Ambient Air Quality Standards. To assess the effectiveness of the regulations and the impacts of economic drivers, we investigated the PM2.5 source trends at 11 sites in New York and New Jersey for the 2010–2019 period. Dispersion-normalized positive matrix factorization (DN-PMF) was used to reduce the meteorological impacts on the source apportionment. The Theil-Sen nonparametric estimator and piecewise linear regression were applied to the resolved source contributions to quantify the trends. While there is a consistent overall decrease of PM2.5 at all sites, increases were observed for some specific PM sources. Secondary Sulfate increased after 2017 at most sites based on the piecewise analysis. Similarly, recent-year increases were resolved for Diesel concentrations likely due to increasing vehicle miles traveled for heavy-duty vehicles. Back trajectory analysis of the OP-Rich source identified southeastern U.S. as a likely major source area, while Biomass Burning has a mixed source from local domestic heating as well as transported from proscribed and wildfires in the southeastern U.S.

An assessment of atmospheric concentrations and spatiotemporal variation of BTEX and associated pollutants in India

Benzene, toluene, ethylbenzene, and xylene (BTEX) pollution poses a serious threat to public health and the environment because of its respiratory and neurological effects, carcinogenic properties, and adverse effects on air quality. BTEX exposure is a matter of grave concern in India owing to the growing vehicular and development activities, necessitating the assessment of atmospheric concentrations and their spatial variation. This paper presents a comprehensive assessment of ambient concentrations and spatiotemporal variations of BTEX in India. The study investigates the correlation of BTEX with other criteria pollutants and meteorological parameters, aiming to identify interrelationships and diagnostic indicators for the source characterization of BTEX emissions. Additionally, the paper categorizes various regions in India according to the Air Quality Index (AQI) based on BTEX pollution levels. The results reveal that the northern zone of India exhibits the highest levels of BTEX pollution compared to central, eastern, and western regions. In contrast, the southern zone experiences the least pollution with BTEX. Seasonal analysis indicates that winter and post-monsoon periods, characterized by lower temperatures, are associated with higher BTEX levels due to the accumulation of localized emissions. When comparing the different zones in India, high traffic emissions and localized activities, such as solvent use and solvent evaporation, are found to be the primary sources of BTEX. The findings of the current study aid in source characterization and identification, and better understanding of the region's air quality problems, which helps in the development of focused BTEX pollution reduction and control strategies.

Airborne Exposure to Pollutants and Mental Health: A Review with Implications for United States Veterans.

Inhalation of airborne pollutants in the natural and built environment is ubiquitous; yet, exposures are different across a lifespan and unique to individuals. Here, we reviewed the connections between mental health outcomes from airborne pollutant exposures, the biological inflammatory mechanisms, and provide future directions for researchers and policy makers. The current state of knowledge is discussed on associations between mental health outcomes and Clean Air Act criteria pollutants, traffic-related air pollutants, pesticides, heavy metals, jet fuel, and burn pits. Although associations between airborne pollutants and negative physical health outcomes have been a topic of previous investigations, work highlighting associations between exposures and psychological health is only starting to emerge. Research on criteria pollutants and mental health outcomes has the most robust results to date, followed by traffic-related air pollutants, and then pesticides. In contrast, scarce mental health research has been conducted on exposure to heavy metals, jet fuel, and burn pits. Specific cohorts of individuals, such as United States military members and in-turn, Veterans, often have unique histories of exposures, including service-related exposures to aircraft (e.g. jet fuels) and burn pits. Research focused on Veterans and other individuals with an increased likelihood of exposure and higher vulnerability to negative mental health outcomes is needed. Future research will facilitate knowledge aimed at both prevention and intervention to improve physical and mental health among military personnel, Veterans, and other at-risk individuals.

Air Quality and Health Impacts of Onshore Oil and Gas Flaring and Venting Activities Estimated Using Refined Satellite-Based Emissions.

Emissions from flaring and venting (FV) in oil and gas (O&G) production are difficult to quantify due to their intermittent activities and lack of adequate monitoring and reporting. Given their potentially significant contribution to total emissions from the O&G sector in the United States, we estimate emissions from FV using Visible Infrared Imaging Radiometer Suite satellite observations and state/local reported data on flared gas volume. These refined estimates are higher than those reported in the National Emission Inventory: by up to 15 times for fine particulate matter (PM2.5), two times for sulfur dioxides, and 22% higher for nitrogen oxides (NOx). Annual average contributions of FV to ozone (O3), NO2, and PM2.5 in the conterminous U.S. (CONUS) are less than 0.15%, but significant contributions of up to 60% are found in O&G fields with FV. FV contributions are higher in winter than in summer months for O3 and PM2.5; an inverse behavior is found for NO2. Nitrate aerosol contributions to PM2.5 are highest in the Denver basin whereas in the Permian and Bakken basins, sulfate and elemental carbon aerosols are the major contributors. Over four simulated months in 2016 for the entire CONUS, FV contributes 210 additional instances of exceedances to the daily maximum 8-hr average O3 and has negligible contributions to exceedance of NO2 and PM2.5, given the current form of the national ambient air quality standards. FV emissions are found to cause over $7.4 billion in health damages, 710 premature deaths, and 73,000 asthma exacerbations among children annually.

Quantifying Multipollutant Health Impacts Using the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE): A Case Study in Atlanta, Georgia.

Air pollution risk assessments do not generally quantify health impacts using multipollutant risk estimates, but instead use results from single-pollutant or copollutant models. Multipollutant epidemiological models account for pollutant interactions and joint effects but can be computationally complex and data intensive. Risk estimates from multipollutant studies are therefore challenging to implement in the quantification of health impacts. Our objective was to conduct a case study using a developmental multipollutant version of the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) to estimate the health impact associated with changes in multiple air pollutants using both a single and multipollutant approach. BenMAP-CE was used to estimate the change in the number of pediatric asthma emergency department (ED) visits attributable to simulated changes in air pollution between 2011 and 2025 in Atlanta, Georgia, applying risk estimates from an epidemiological study that examined short-term single-pollutant and multipollutant (with and without first-order interactions) exposures. Analyses examined individual pollutants (i.e., ozone, fine particulate matter, carbon monoxide, nitrogen dioxide (), sulfur dioxide, and particulate matter components) and combinations of these pollutants meant to represent shared properties or predefined sources (i.e., oxidant gases, secondary pollutants, traffic, power plant, and criteria pollutants). Comparisons were made between multipollutant health impact functions (HIF) and the sum of single-pollutant HIFs for the individual pollutants that constitute the respective pollutant groups. Photochemical modeling predicted large decreases in most of the examined pollutant concentrations between 2011 and 2025 based on sector specific (i.e., source-based) estimates of growth and anticipated controls. Estimated number of avoided asthma ED visits attributable to any given multipollutant group were generally higher when using results from models that included interaction terms in comparison with those that did not. We estimated the greatest number of avoided pediatric asthma ED visits for pollutant groups that include (i. e., criteria pollutants, oxidants, and traffic pollutants). In models that accounted for interaction, year-round estimates for pollutant groups that included ranged from 27.1 [95% confidence interval (CI): 1.6, 52.7; traffic pollutants] to 55.4 (95% CI: 41.8, 69.0; oxidants) avoided pediatric asthma ED visits. Year-round results using multipollutant risk estimates with interaction were comparable to the sum of the single-pollutant results corresponding to most multipollutant groups [e.g., 52.9 (95% CI: 43.6, 62.2) for oxidants] but were notably lower than the sum of the single-pollutant results for some pollutant groups [e.g., 77.5 (95% CI: 66.0, 89.0) for traffic pollutants]. Performing a multipollutant health impact assessment is technically feasible but computationally complex. It requires time, resources, and detailed input parameters not commonly reported in air pollution epidemiological studies. Results estimated using the sum of single-pollutant models are comparable to those quantified using a multipollutant model. Although limited to a single study and location, assessing the trade-offs between a multipollutant and single-pollutant approach is warranted. https://doi.org/10.1289/EHP12969.

Assessment of the Ambient Air Quality of Sangli City, Maharashtra, vis-à-vis National Ambient Air Quality Standards (NAAQS)

Assessment of ambient air quality is important to formulate short-term and long-term plans to mitigate air pollution. This study attempts to present an ambient air quality assessment for Sangli City, located at 16.85° N, and 74.58° E. The assessment is done through ambient air quality data measured over three ambient air quality monitoring stations located in residential, commercial, and industrial zones of Sangli City. The data was collected from 2009 to 2017 under the National Air Quality Monitoring Program of the Central Pollution Control Board (CPCB), India. The observed concentrations of gaseous pollutants, SO2 and NOx, and respirable particulate matter are compared with the concentration levels prescribed by the National Ambient Air Quality Standards (NAAQS) in 2009. The concentration levels of particulate matter are quite on the higher side of the prescribed standards. The levels of NOx are almost approaching the prescribed values, whereas SO2 levels are well within the margin. We have also studied seasonal variations in the pollutants during the study period. In general, the air quality in Sangli City is quite undesirable during the mid-winter to mid-summer period. The study is useful for common men to increase awareness. It also forms baseline data and is useful for local and central government to design appropriate strategies to apply corrective measures, protect health, and promote environmental protection.

Investigating the Potential Impact of Air Pollution on Alzheimer's Disease and the Utility of Multidimensional Imaging for Early Detection.

Pollution is ubiquitous, and much of it is anthropogenic in nature, which is a severe risk factor not only for respiratory infections or asthma sufferers but also for Alzheimer's disease, which has received a lot of attention recently. This Review aims to investigate the primary environmental risk factors and their profound impact on Alzheimer's disease. It underscores the pivotal role of multidimensional imaging in early disease identification and prevention. Conducting a comprehensive review, we delved into a plethora of literature sources available through esteemed databases, including Science Direct, Google Scholar, Scopus, Cochrane, and PubMed. Our search strategy incorporated keywords such as "Alzheimer Disease", "Alzheimer's", "Dementia", "Oxidative Stress", and "Phytotherapy" in conjunction with "Criteria Pollutants", "Imaging", "Pathology", and "Particulate Matter". Alzheimer's disease is not only a result of complex biological factors but is exacerbated by the infiltration of airborne particles and gases that surreptitiously breach the nasal defenses to traverse the brain, akin to a Trojan horse. Various imaging modalities and noninvasive techniques have been harnessed to identify disease progression in its incipient stages. However, each imaging approach possesses inherent limitations, prompting exploration of a unified technique under a single umbrella. Multidimensional imaging stands as the linchpin for detecting and forestalling the relentless march of Alzheimer's disease. Given the intricate etiology of the condition, identifying a prospective candidate for Alzheimer's disease may take decades, rendering the development of a multimodal imaging technique an imperative. This research underscores the pressing need to recognize the chronic ramifications of invisible particulate matter and to advance our understanding of the insidious environmental factors that contribute to Alzheimer's disease.

Large Decreases in Tailpipe Criteria Pollutant Emissions from the U.S. Light-Duty Vehicle Fleet Expected in 2020-2040.

The U.S. EPA MOVES3 model was used to assess the impact of the large-scale introduction of electric vehicles on emissions of criteria pollutants (CO, hydrocarbons [HC], NOx, and particulate matter [PM]) and CO2 from the U.S. light-duty vehicle fleet. Large reductions in emissions of these criteria pollutants occurred in 2000-2020. These trends are expected to continue through 2040 driven by turnover of the conventional fleet with old vehicles being replaced by battery electric vehicles (BEVs) and by new internal combustion engine vehicles (ICEVs) with modern emission control systems. Without the introduction of BEVs, the absolute emissions of CO, NOx, HC, and PM2.5 from the U.S. light-duty vehicle fleet are expected to decrease by approximately 61, 88, 55, and 20% from 2020 to 2040. Introduction of BEVs with market share increasing linearly to 100% in 2040 provides additional benefits, which, combined with ICEV fleet turnover, would lead to decreases of absolute emissions of CO, NOx, HC, and PM2.5 of approximately 77, 94, 71, and 37% from 2020 to 2040. Reductions in CO2 emissions follow a similar pattern. Large decreases in criteria pollutant and CO2 emissions from light duty vehicles lie ahead.

Estimation of hourly one square kilometer fine particulate matter concentration over Thailand using aerosol optical depth

In the recent years, concentration of fine particulate matter that are 2.5 microns or less in diameter (PM2.5) in Thailand has consistently exceeded the national ambient air quality standard. Currently, the measurement of PM2.5 concentration relies on air quality monitoring stations operated by the Pollution Control Department of Thailand (PCD). However, these stations are insufficient, particularly in rural areas, where agricultural open burning are major sources of pollution after harvesting period. This study aims to enhance the monitoring of PM2.5 concentration by leveraging cost-effective technologies. We propose the integration of satellite data, specifically Aerosol Optical Depth (AOD) from Multi-Angle Atmospheric Correction (MAIAC) product and Himawari-8 satellites, with the Weather Research and Forecasting Model (WRF) data, to provide supplementary data to the ground-based monitoring. Hourly 5 × 5 km2 AOD data from Himawari-8 were downscaled to a high-resolution of 1 × 1 km2, leveraging the AOD distribution pattern of the concurrent MAIAC product using eXtreme Gradient Boosting (XGBoost) model. Notably, during Thailand’s rainy season (May to August), the study observed a relative reduction in the training model’s R-square value. This phenomenon is attributed to temporal discrepancies between Himawari-8 and the MAIAC products during this period. The predictive models of PM2.5 concentrations with the identification of pertinent variables through Pearson’s correlation analysis and recursive feature elimination, driven by the robust XGBoost model. Subsequently, the downscaled AOD, wind speed, temperature, and pressure were identified as predictors for the estimation of hourly PM2.5 concentration. This comprehensive approach enabled the projection of PM2.5 levels across Thailand, encompassing over 600,000 grids at 1 × 1 km2 resolution. The developed models, thus, offer a valuable tool for robust and high-resolution PM2.5 concentration estimation, presenting significant implications for air quality monitoring and management in Thailand.

Ambient Air Pollution Monitoring and Health Studies Using Low-Cost Internet of Things Monitor within KNUST Community

Urban environments with high industrialization are infested with hazardous chemicals and airborne pollutants. These pollutants CO, O3, SO2, NO2, and PM can have devastating effects on human health, causing both acute and chronic diseases such as respiratory infections, lung cancer, and heart disease. Air pollution monitoring is vital to warn citizens of the health risks associated with exposure to high concentrations of these criteria pollutants. This study designed a low-cost IoT monitor to measure concentration levels of criteria pollutants emitted from transportation sources within Kwame Nkrumah University of Science and Technology environs. Three monitoring sites, KNUST Tech junction, Ayeduase gate junction and KNUST campus junction, were identified as the locations within the proximity of the University for the Deployment of the monitor. Hourly and mean daily CO, NO2, O3 and SO2 concentrations at each of the three sites were measured for a week using the IoT monitor, when students were in school and when students were on vacation. The average daily CO, NO2 and O3 concentrations measured at the selected locations when school was in session and during vacation were presented on histogram. The mean weekly concentrations of CO, NO2 and O3 were also estimated as 13.2ppm, 0.277ppm and 0.106ppb respectively at KNUST Tech junction; 10.1ppm, 0.254ppm and 0.110ppb respectively at Ayeduase gate junction; and 8.0ppm, 0.415ppm and 0.100ppb respectively at the KNUST campus junction when school was in session. The results show that the concentrations of all the pollutants were higher and exceeded the EPA standards except for CO at KNUST Campus junction monitoring site. These high levels of emissions is an indication of a health concern for the students at the university and university authorities can device means of curbing it.