Major Source Of Air Pollution Research Articles

Diesel Particulate Matter Permeation into Normal Human Skin and Intervention Using a Topical Ceramide Formulation

Introduction: Diesel particulate matter (DPM) emitted from diesel engines is a major source of air pollutants. DPM is composed of elemental carbon, which adsorbs organic compounds including toxic polycyclic aromatic hydrocarbons (PAHs). The skin, as well as airways, is directly exposed to DPM, and association of atopic dermatitis, psoriasis flares, and premature skin aging with air pollutant levels has been documented. In skin, the permeation of DPM and DPM-adsorbed compounds is primarily blocked by the epidermal permeability barrier deployed in the stratum corneum. Depending upon the integrity of this barrier, certain amounts of DPM and DPM-adsorbed compounds can permeate into the skin. However, this permeation into human skin has not been completely elucidated. Methods: We assessed the permeation of PAHs (adsorbed to DPM) into skin using ex vivo normal (barrier-competent) organ-cultured human skin after application of DPM. Two major PAHs, 2-methylnaphthalene and triphenylene, and a carcinogenic PAH, benzo(a)pyrene, all found in DPM, were measured in the epidermis and dermis using liquid chromatography electrospray ionization tandem mass spectrometry. In addition, we investigated whether a topical formulation can attenuate the permeation of DPM into skin. Results: 2-Methylnaphthalene, triphenylene, and benzo(a)pyrene were recovered from the epidermis. Although these PAHs were also detected in the dermis after DPM application, these PAH levels were significantly lower than those found in the epidermis. We also demonstrated that a topical formulation that has the ability to form more uniform membrane structures can significantly suppress the permeation of PAHs adsorbed to DPM into the skin. Conclusion: Toxic compounds adsorbed by DPM can permeate even barrier-competent skin. Hence, barrier-compromised skin, such as in atopic dermatitis, psoriasis, and xerosis, is even more vulnerable to air pollutants. A properly formulated topical mixture that forms certain membrane structures on the skin surface can effectively prevent permeation of exogenous substances, including DPM, into skin.

Environmental and Health Impact of Heavy Metal Accumulation in (Hair - Nails) of Scavenger Workers at Some Landfill Sites in Baghdad City-Iraq

Background: Heavy metals are considered one of the most dangerous environmental pollutants due to their high toxicity, even at low concentrations, and their accumulation in various body tissues. Landfills contain various wastes rich in heavy metals, such as batteries, paints, and household and hospital waste, making them a major source of air, soil, and groundwater pollution. Objective: To analyze the level of accumulation of heavy metals in hair and nail samples of waste collectors. Material and method: Five main landfill sites in the city of Baghdad were chosen to collect hair and nail samples from waste collectors of both sexes for the winter and summer seasons to analyze the levels of metal concentrations (lead, cadmium, nickel, zinc, and copper). And the use of atomic absorption spectrometry (AAS). Results: Higher concentrations of lead, copper, and nickel were found in nail samples during the summer compared to the winter, indicating an increased risk of exposure to them. While zinc accumulation was much greater in winter samples. Conclusions The results highlight the health risks faced by landfill workers. A need to develop tailored strategies to control exposure to these emissions based on weather conditions and priority metals of concern identified through regular biological monitoring.

Internalization of External Cost in the Thermal Power Generation on Social Welfare Maximization

For decades, Kenya has incorporated thermal power technology into its grid to generate electrical energy using fossil fuels such as petroleum, natural gas, and coal. The burning of fossil fuels has become a major source of air pollutants and is associated with several undesirable side effects on the environment and human health. However, the impact of pollutants on environmental sustainability and public welfare has yet to be evaluated. Therefore, the purpose of this study is to evaluate the external cost of electricity generated by thermal power plants in Kenya. Both survey data and secondary data were used. The analysis was conducted using externality valuation and welfare maximization approaches, and the research hypotheses were tested using a negative binomial regression model. The annual external cost ($/2022) was determined to be $ 1,333,904,970.76, with the following distribution: environmental at $ 993,488,336.26, Public health at $ 86,760,038.01, and socio-economic at $ 253,656,596.49. Equally, the thermal power generation marginal social cost ($/2022) was determined to be 1.22 $cents/kWh with the following distribution: Marginal Private Cost (MPC) at 0.01 $cents/kWh and Marginal External Cost (MEC) at 1.21 $cents/kWh. The established marginal social cost (MSC) (i.e. Σ MPC+MEC) was 1.22 ($cents/kWh). Thus, MSC is significantly greater than the established social marginal benefit (SMB) of 0.089 ($ cents/kWh); hence, we conclude that the burden of social welfare loss is highly significant, making thermal power a non-sustainable and economic energy source.

Exploring the relationships between 3D urban landscape patterns and PM2.5 pollution using the multiscale geographic weighted regression model

Fine particulate matter (PM2.5) is a major source of air pollution and exerts serious impacts on human health. The 3D urban landscape patterns can significantly affect the diffusion and emissions of PM2.5. However, studies on the relationships between 3D urban landscape patterns and PM2.5 pollution across different seasons remain understudied. With the ground-level air pollutants estimated by the remote sensing and fine-scale building information, this study applied the multiscale geographically weighted regression model to explore such relationships. Wuhan, the largest metropolis in Central China, was selected as the study area for the application of our methodology. The results showed that the direction, degree, and scale of the effect of 3D urban landscape patterns on PM2.5 pollution varied across seasons. For building height, the standard deviation of building height had a significant positive correlation with PM2.5 all year round. For building density, the building count density showed a significant positive correlation with PM2.5 in general, with the bandwidth in winter and autumn smaller than in spring and summer. The building plan area fraction exerted both positive and negative influences on PM2.5, dependent on season and location. The bandwidth of it gradually increased from spring to winter, with the effect changing from local to regional scale. For building volume, the floor area ratio showed a significant negative correlation with PM2.5 in winter and autumn, and a localized effect was found, especially in winter. The findings of this study provide practical implications for urban planning and policy making to mitigate PM2.5 pollution in the rapidly urbanizing regions.

A Flexible Cross-Efficiency Model with Partial Preference for Efficiency Evaluation of Clean Transportation Energy

The rapid development of the transportation industry benefits from the consumption of energy, but the excessive dependence on petroleum fuels makes it a major source of air pollution. In order to achieve green and high-quality development of the transportation industry, many countries are committed to scientifically evaluating the utilization efficiency of clean energy, which has attracted wide attention from the whole society. Significantly, without considering the diversity and complexity of pollutants, indicators used in previous studies were unable to cover all pollutants when establishing the evaluation index system. Meanwhile, as an efficient tool, data envelopment analysis (DEA) is extensively used when it comes to efficiency evaluation. However, the absolute preference of existing benevolent and aggressive cross-efficiency models limits its application scenarios. To address the challenges above, an improved flexible cross-efficiency DEA model is proposed considering both same and different benevolence coefficients of decision-making units (DMUs) on the basis of pointing out the inadequacy of the previous model. The concepts of consensus coefficient and group preference are introduced in the aggregation of cross-efficiency. Besides, based on the theory of undesirable output, the consumption of nonclean energy is taken into account as the input indicator to characterize the degree of pollution. The results show that the obtained cross-efficiency value and efficiency ranking of clean transportation energy change sensitively under various benevolent coefficients. There is an important practical significance to consider the independent preference information of DMUs for the evaluation and ranking of cross-efficiency.

Assessing total cost of driving competitiveness of zero-emission trucks

Medium- and heavy-duty vehicles are 21% of US transportation greenhouse gas (GHG) emissions and a major source of air pollution. We explore how the total cost of driving (TCD) of zero-emission vehicles (ZEVs), including battery electric vehicles and hydrogen fuel cell electric vehicles (EVs and FCEVs), could evolve under alternative scenarios. With continued improvements in vehicles and fuels, ZEVs can rapidly become viable, potentially reaching TCD parity or better compared to diesel vehicles by 2035 for all market segments. For heavy long-haul trucks, EVs become competitive on a TCD basis at charging costs below $0.18/kWh, while FCEVs become competitive on a TCD basis at hydrogen costs below $5/kg. A full transition to ZEV sales by 2035 results in 65% emissions reductions by 2050 compared to 2019 without supportive policies. Incentives such as the Inflation Reduction Act vehicle purchase credits further accelerate ZEV TCD competitiveness with major adoption opportunities over the next five years.

Health risks of exposure to air pollution in areas where coal-fired power plants are located: protocol for a scoping review

IntroductionCoal-fired power plants are major sources of air pollution which impact human health. Coal combustion byproducts released into the air include particulate matter, nitrogen oxides and sulphur dioxide. Exposure to...

Was the low-fare public transport in Bonn a success? An evaluation of the climate ticket users and lessons for transportation companies

The transport sector is a major source of air pollution and thus a major contributor to the changing climate. As a result, in the recent past, driving bans have been imposed on cars with critical pollutant groups. As an international UN campus and self-proclaimed climate capital, the Federal City of Bonn declared a climate emergency in 2019 and participated in a federally funded “Lead City” project to optimise air quality. A key goal of the project is to reduce private motorised transport and strengthen public transport. Among the implemented measures, a “climate ticket” was introduced in 2019 whereby consumers could purchase an annual 365 € ticket for all local public transport. This paper reports on an analysis of that ticket’s changes in travel behavior.A quantitative survey (n = 1,315) of the climate ticket users as well as the multiple regressions confirm that the climate ticket attracted more customers to the buses and trams and that a modal shift for the period of the measure was recognisable. The multiple regressions showed that the ticket was perceived significantly more positively by full-time employed users than by unemployed people. The results also show that, in addition to the price, it is essential that travel time and reliability are ensured. Furthermore, the eligible groups of people, the area of coverage, and good connecting services should be extended. To sustainably improve air quality, this type of mobility service must be optimised and introduced on a permanent basis.

Indoor Air Pollution in Kenya

AbstractIndoor air pollution is an environmental health challenge in Kenya, particularly in rural households, and low-income urban areas. This review aims to provide an overview of the sources, health effects and mitigation strategies for indoor air pollutants in Kenya. The main goal of our study was to review existing literature on indoor air pollution in Kenya with the aim of identifying research gaps for future research. Our methodology involved a critical examination of the existing literature review. This is because traditional fuel burning for cooking and heating, and kerosene lamps are major sources of indoor air pollution. Exposure to air pollutants can lead to respiratory and cardiovascular disease among women and children who are more vulnerable. Despite efforts to improve indoor air quality, significant challenges remain including access to clean fuels and technologies, inadequate infrastructure, and low awareness of health impact of indoor air pollution. Mitigation strategies include the transition to cleaner cooking sources, solar lamps for lighting and education campaigns on health impacts. The review concludes that a multifaceted approach involving various stakeholders is necessary to effectively address indoor air pollution in Kenya and improve public health.

Costs of Coal Abatement for Residential Heating to Reduce Urban Air Pollution in Asian Russia: Evidence from Krasnoyarsk

Heat and electricity generation are major sources of air pollution in many large cities worldwide. In Siberian cities, the heat supply significantly contributes to air pollution, as coal remains the primary energy fuel in most of Asian Russia. Krasnoyarsk, the second-largest city in the macro-region by population, serves as a clear example of urban air pollution. In recent years, public authorities have implemented several measures to reduce pollutant emissions. These measures include modernizing thermal power stations, replacing inefficient small boiler houses, and converting residential heating to more environmentally friendly types of fuel. However, our analysis shows that these policies have not yet resulted in a significant transition away from using coal for heating. One of the primary reasons is that alternative fuels are unable to compete with coal in terms of price. The proposal suggests transitioning private households to pellet heating. Our goal is to evaluate the potential environmental and economic impacts of this measure. We estimate the necessary subsidy size for the successful implementation of both initiatives. In conclusion, converting households from coal to pellet heating can reduce the emissions of nitrogen oxides, carbon monoxide, sulfur oxides, and suspended particles by 0.2%, 8.5%, 4.4%, and 2.7%, respectively, of the total pollutant emissions in Krasnoyarsk. Furthermore, this conversion can provide economic benefits by supporting local pellet producers who heavily rely on export markets. If practically implemented, the proposed approach could solve one of the most significant development issues of one of the largest Russian cities within a few years, with only 2.5% of the annual city budget expenditure.

Overcoming Common Pitfalls to Improve the Accuracy of Crop Residue Burning Measurement Based on Remote Sensing Data

Crop residue burning (CRB) is a major source of air pollution in many parts of the world, especially Asia. Policymakers, practitioners, and researchers have invested in measuring the extent and impacts of burning and developing interventions to reduce its occurrence. However, any attempt to measure burning, in terms of its extent, impact, or the effectiveness of interventions to reduce it, requires data on where burning occurs. These data are challenging to collect in the field, both in terms of cost and feasibility, because crop-residue fires are short-lived, each covers only a small area, and evidence of burning disappears once fields are tilled. Remote sensing offers a way to observe fields without the complications of on-the-ground monitoring. However, the same features that make CRB hard to observe on the ground also make remote-sensing-based measurements prone to inaccuracies. The extent of crop burning is generally underestimated due to missing observations, while individual plots are often falsely identified as burned due to the local dominance of the practice, a lack of training data on tilled vs. burned plots, and a weak signal-to-noise ratio that makes it difficult to distinguish between the two states. Here, we summarize the current literature on the measurement of CRB and flag five common pitfalls that hinder analyses of CRB with remotely sensed data: inadequate spatial resolution, inadequate temporal resolution, ill-fitted signals, improper comparison groups, and inadequate accuracy assessment. We take advantage of data from ground-based monitoring of CRB in Punjab, India, to calibrate and validate analyses with PlanetScope and Sentinel-2 imagery and illuminate each of these pitfalls. We provide tools to assist others in planning and conducting remote sensing analyses of CRB and stress the need for rigorous validation.

Minimize population exposure to vehicle-generated emissions by road pricing

Vehicular emissions, as a major source of urban air pollution, are threatening public health. With a goal to regulate population exposure to vehicular emissions, three bilevel road pricing models are established to minimize the exposure to vehicular emissions for on-road travelers, near-road residents, and all populations in the study network. A simulated annealing-based method is applied to solve these models based on a first-best pricing scheme. For ease of implementation, a second-best pricing scheme is then proposed and solved based on a meta-heuristic approach coupled with genetic algorithm with elitism and simulated annealing. The proposed method is tested using the Nguyen-Dupuis network and the Sioux Falls network. It is found that a tradeoff exists between the objectives of minimizing the emission exposure levels of travelers and near-road residents. Another tradeoff is found between the objectives of minimizing total population exposure and total travel time. Results from the numerical example indicate that the optimality results obtained in the first-best pricing scheme may also be approximated by the second-best pricing scheme with fewer tolling locations and less toll collection. The conclusions drawn from the paper could potentially assist policymakers in formulating road pricing policies that prioritize environmental concerns.

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.

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.

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.

Biomass smoke exposure and somatic growth among children: The RESPIRE and CRECER prospective cohort studies in rural Guatemala

BackgroundCooking-related biomass smoke is a major source of household air pollution (HAP) and an important health hazard. Prior studies identified associations between HAP exposure and childhood stunting; less is known for underweight and wasting. Few studies had personal HAP measurements. Methods557 households in rural Guatemala were enrolled in the CRECER study, the follow-up study of the RESPIRE randomized intervention trial. They were assigned to three groups that received chimney stoves at different ages of the study children. Multiple personal carbon monoxide (CO) exposure measurements were used as proxies for HAP exposures. Children’s heights and weights were measured from 24 to 60 months of age. Height-for-age z-score (HAZ), weight-for-age z-score (WAZ), and weight-for-height z-score (WHZ) were calculated based on the World Health Organization’s Multicentre Growth Reference Study. HAZ, WAZ, and WHZ below −2 were classified as stunting, underweight, and wasting, respectively. Generalized linear models and mixed effects models were applied. Results541 children had valid anthropometric data, among whom 488 (90.2 %) were stunted, 192 (35.5 %) were underweight, and 2 (0.3 %) were wasted. A 1 ppm higher average CO exposure was associated with a 0.21 lower HAZ (95 % CI: 0.17–0.25), a 0.13 lower WAZ (95 % CI: 0.10–0.17) and a 0.06 lower WHZ (95 % CI: 0.02–0.10).The associations for HAZ were stronger among boys (coefficient = −0.29, 95 % CI: −0.35 – −0.22) than among girls (coefficient = −0.15, 95 % CI: −0.20 – −0.10). A 1 ppm-year higher cumulative CO exposure was associated with a higher risk of moderate stunting among boys (OR = 1.27, 95 % CI: 1.05-1.59), but not among girls. DiscussionIn this rural Guatemalan population, higher HAP exposure was associated with lower HAZ and WAZ. The associations between HAP and HAZ/stunting were stronger among boys. Reducing HAP might benefit childhood somatic growth in rural populations of low-income countries.

Towards a Greener and Fairer Transportation System: A Survey of Route Recommendation Techniques

In recent years, ride-hailing services have emerged as a popular means of transportation for the residents of urban areas. There is an inequality in the spatio-temporal distribution of demand and supply, which requires the proper recommendation of routes to drivers in order to guide them towards riders optimally. This paper provides a review of different route recommendation strategies that have been applied in ride-hailing platforms with the main focus on fairness, and environmental issues. It is important to consider the environmental aspects of route recommendation systems as the transportation sector is one of the major sources of air pollution and has reduced the life expectancy of people around the globe. Moreover, there is an unfair distribution of resources and opportunities among the drivers and riders of the platform which has affected their long-term sustainability in the market. In this paper, we highlight the critical challenges and opportunities inherent in the design of green and fair route recommendation systems and indicate some possible directions for future research.

USEPA CALPUFF validation and dispersion modeling of beef cattle feedlot PM10 and PM2.5 emissions factors

Beef cattle feedlots are major sources of criteria air pollutants in the United States. To aid stakeholders in air quality modeling, emissions factors for animal waste-producing facilities are under development by the U.S. Environmental Protection Agency (USEPA). However, beef cattle feeding operations (feedlots) are not among facilities surveyed under the associated USEPA National Air Emissions Monitoring Study (NAEMS). Authors aim to supplement NAEMS through development and validation of particulate matter (PM10 and PM2.5) emission factors from a previous sampling campaign of five large feedlots in the Southern High Plains of North America via USEPA's CALPUFF dispersion model. PM concentrations were higher overall during the warm season (March–August; PM10 = 692 ± 507, PM2.5 = 114 ± 110 μg/m3) relative to cool season (November–January; PM10 = 102 ± 105, PM2.5 = 12.2 ± 8.6 μg/m3), and overall median PM10 and PM2.5 emission factors were 48.2 (day = 33.8, night = 72.4) and 5.22 (day = 8.4, night = 3.1) mg/m2/hr, respectively. Validated emissions factors were then used to model diurnal peaks and long-range transport (100 km maximum extent). Peak concentrations were most frequently observed between 2000 and 0600 h (93.9%; mean 1-hr peak ± SD = 1119 ± 1240 μg/m3) for PM10 and 1600-0600 h (80.0%; mean 1-hr peak ± SD; 178 ± 174 μg/m3) for PM2.5. Four of five feedlots exceeded ≥1 primary PM National Ambient Air Quality Standard (NAAQS) at receptors (2-m height) over a 5-km spatial extent during the modeling period (November 2010–August 2011). Moreover, present estimates of feedlot-derived inhalable PM represent human and environmental exposure pathways currently exempt from Clean Air Act monitoring and reporting standards, and spatial interpolation of federal air quality monitors cannot adequately characterize inhalable PM concentrations in non-metropolitan municipalities and unincorporated rural communities.

Detecting nitrogen oxide emissions in Qatar and quantifying emission factors of gas-fired power plants – a 4-year study

Abstract. Nitrogen oxides (NOx=NO+NO2), produced in urban areas and industrial facilities (particularly in fossil-fuel-fired power plants), are major sources of air pollutants, with implications for human health, leading local and national authorities to estimate their emissions using inventories. In Qatar, these inventories are not regularly updated, while the country is experiencing fast economic growth. Here, we use spaceborne retrievals of nitrogen dioxide (NO2) columns at high spatial resolution from the TROPOspheric Monitoring Instrument (TROPOMI) to estimate NOx emissions in Qatar from 2019 to 2022 with a flux-divergence scheme, according to which emissions are calculated as the sum of a transport term and a sink term representing the three-body reaction comprising NO2 and hydroxyl radical (OH). Our results highlight emissions from gas power plants in the northeast of the country and from the urban area of the capital, Doha. The emissions from cement plants in the west and different industrial facilities in the southeast are underestimated due to frequent low-quality measurements of NO2 columns in these areas. Our top-down model estimates a weekly cycle, with lower emissions on Fridays compared to the rest of the week, which is consistent with social norms in the country, and an annual cycle, with mean emissions of 9.56 kt per month for the 4-year period. These monthly emissions differ from the Copernicus Atmospheric Monitoring Service global anthropogenic emissions (CAMS-GLOB-ANT_v5.3) and the Emissions Database for Global Atmospheric Research (EDGARv6.1) global inventories, for which the annual cycle is less marked and the average emissions are respectively 1.67 and 1.68 times higher. Our emission estimates are correlated with local electricity generation and allow us to infer a mean NOx emission factor of 0.557 tNOx GWh−1 for the three gas power plants in the Ras Laffan area.