Nitrogen Dioxide Concentrations Research Articles

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

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

Geospatial Artificial Intelligence for Estimating Daytime and Nighttime Nitrogen Dioxide Concentration Variations in Taiwan: A Spatial Prediction Model

Nitrogen dioxide (NO2) is a major air pollutant primarily emitted from traffic and industrial activities, posing health risks. However, current air pollution models often underestimate exposure risks by neglecting the bimodal pattern of NO2 levels throughout the day. This study aimed to address this gap by developing ensemble mixed spatial models (EMSM) using geo-artificial intelligence (Geo-AI) to examine the spatial and temporal variations of NO2 concentrations at a high resolution of 50m. These EMSMs integrated spatial modelling methods, including kriging, land use regression, machine learning, and ensemble learning. The models utilized 26 years of observed NO2 measurements, meteorological parameters, geospatial layers, and social and season-dependent variables as representative of emission sources. Separate models were developed for daytime and nighttime periods, which achieved high reliability with adjusted R2 values of 0.92 and 0.93, respectively. The study revealed that mean NO2 concentrations were significantly higher at nighttime (9.60 ppb) compared to daytime (5.61 ppb). Additionally, winter exhibited the highest NO2 levels regardless of time period. The developed EMSMs were utilized to generate maps illustrating NO2 levels pre and during COVID restrictions in Taiwan. These findings could aid epidemiological research on exposure risks and support policy-making and environmental planning initiatives.

Abilities of object monitoring technology for analyzing nitrogen dioxide concentrations in areas where major sources of pollution are located

Abilities of object monitoring technology for analyzing nitrogen dioxide concentrations in areas where major sources of pollution are located

Air pollutants, genetic susceptibility, and incident schizophrenia in later life: A prospective study in the UK Biobank

ObjectiveAir pollution has been linked to multiple psychiatric disorders, but little is known on its long-term association with schizophrenia. The interaction between air pollution and genetic susceptibility on incident schizophrenia has never been reported. We aimed to explore the associations between long-term air pollution exposure and late-onset schizophrenia and evaluate whether genetic susceptibility could modify the association. MethodsThis population-based prospective cohort study included 437,802 middle-aged and elderly individuals free of schizophrenia at baseline in the UK Biobank. Land use regression models were applied in the estimation of the annual average concentrations of nitrogen dioxide (NO2), nitrogen oxides (NOx), fine particulate matter (PM2.5), and inhalable particulate matter (PM10) at residence. The associations between air pollutants and schizophrenia were evaluated by using Cox proportional hazard models. A polygenic risk score of schizophrenia was constructed for exploring potential interaction of air pollutants with genetic susceptibility. ResultsAn interquartile range increase in PM2.5, PM10, NO2, and NOx was associated with the hazard ratios (HR) for incident schizophrenia at 1.19, 1.16, 1.22, and 1.09, respectively. The exposure-response curves for the association of air pollution with incident schizophrenia were approximately linear. There are additive interactions of air pollution score (APS), PM10, NO2, and NOx with genetic risk. Specifically, compared with participants with low genetic susceptibility and low APS, the HR was 3.23 for individuals with high genetic risk and high APS, among which 0.49 excess risk could be attributed to the additive interaction, accounting for 15 % of the schizophrenia risk. ConclusionThis large-scale, prospective cohort study conveys the first-hand evidence that long-term air pollution exposure could elevate schizophrenia incidence in later life, especially for individuals with higher genetic risks. The findings highlight the importance of improving air quality for preventing the late-onset schizophrenia in an aging era, especially among those with high genetic risks.

TIME-SERIES ANALYSIS OF SATELLITE-BASED TRACE GASES CONCENTRATIONS IN PORT AREAS IN THE PHILIPPINES USING SENTINEL-5P

Abstract. This study examined the temporal variations of Nitrogen Dioxide (NO2) and Sulfur Dioxide (SO2) concentrations retrieved from Sentinel-5P TROPOMI in six port areas in the Philippines, representing different climate types. The results demonstrate that the COVID-19 lockdowns led to a significant decrease in NO2 levels, while SO2 concentrations varied due to factors such as volcanic activity. Seasonal analysis of NO2 and SO2 retrievals, considering the four climate types in the Philippines, revealed that the wet season values for both pollutants were higher than the dry season values for most ports. This disparity contradicted the general trend of higher air pollutant levels during the dry season and lower levels during the wet season. The authors attributed this to the nature of port activities during inclement weather, where ships are more likely to be docked with continuously operating engines, leading to continuous emissions within the port buffer zones. Temporal analysis and time series decomposition analysis revealed patterns and trends, identifying the months of minimum and maximum pollutant concentrations and the overall trend throughout the study period.

Air quality on UK diesel and hybrid trains

Concentrations of particulate matter (PM10, PM2.5), ultrafine (UFP), particle number (PNC), black carbon (BC), nitrogen dioxide (NO2) and nitrogen oxides (NOX) were measured in train carriages on diesel and bi-mode trains on inter-city and long-distance journeys in the United Kingdom (UK) using a high-quality mobile measurement system. Air quality on 15 different routes was measured using highly-time resolved data on a total of 119 journeys during three campaigns in winter 2020 and summer 2021; this included 13 different train classes. Each journey was sampled 4–10 times with approximatively 11,000 min of in-train concentrations in total. Mean-journey concentrations were 7.552 µg m−3 (PM10); 3.936 µg m−3 (PM2.5); 333–11,300 # cm−3 (PNC); 225–9,131 # cm−3 (UFP); 0.6–11 µg m−3 (BC); 28–201 µg m−3 (NO2); and 130–3,456 µg m−3 (NOX). The impact of different factors on in-train concentrations was evaluated. The presence of tunnels was the factor with the largest impact on the in-train particle concentrations with enhancements by a factor of 40 greater than baseline for BC, and a factor 6 to 7 for PM and PNC. The engine fuel mode was the factor with the largest impact on NO2 with enhancements of up to 14-times larger when the train run on diesel compared to the times running on electric on hybrid trains. Train classes with an age < 10 years observed the lowest in-train PM, BC and NOX concentrations reflecting improvements in aspects of rail technology in recent years. Air quality on UK diesel trains is higher than ambient concentrations but has lower PM2.5 and PNC than most other transport modes, including subway systems, diesel and petrol cars. This paper adds significantly to the evidence on exposure to poor air quality in transport micro-environments and provides the industry and regulatory bodies with reference-grade measurements on which to establish in-train air quality guidelines.

Seasonal Variations Assessment of Air Pollutants of Communities in the Vicinity of Scrap Metal Recycling Industries in Ogijo, Shagamu South LGA, Ogun State, SW Nigeria

This manuscript presents a thorough investigation into the seasonal variations of air pollutants in communities surrounding scrap metal recycling industries in Ogijo, Shagamu South LGA, Ogun State, Nigeria utilizing advanced Gary Wolf Environmental Sensing and Particulate Counting devices. The study meticulously measures concentrations of carbon monoxide, nitrogen dioxide, sulfur dioxide, PM2.5, and PM10 during both dry and wet seasons across 20 strategic sampling locations and control. The findings reveal significant seasonal fluctuations in pollutant levels, with some concentrations exceeding Nigerian ambient air quality standards, highlighting a pressing environmental health concern. The study not only underscores the critical need for regulatory oversight and the implementation of safer metal scrapping practices but also advocates for regular environmental monitoring to mitigate the adverse impacts of such industries on local communities and the environment.

Air pollution and diabetes mellitus: Association and validation in a desert area in China.

Despite the growing evidence pointing to the detrimental effects of air pollution on diabetes mellitus (DM), the relationship remains poorly explored, especially in desert-adjacent areas characterized by high aridity and pollution. We conducted a cross-sectional study with health examination data from over 2.9 million adults in two regions situated in the southern part of the Taklamakan Desert, China. We assessed three-year average concentrations (2018-2020) of particulate matter (PM1, PM2.5, and PM10), carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2) through a space-time extra-trees model. After adjusting for various covariates, we employed generalized linear mixed models to evaluate the association between exposure to air pollutants and DM. The odds ratios for DM associated with a 10 µg/m3 increase in PM1, PM2.5, PM10, CO, and NO2 were 1.898 (95% CI: 1.741, 2.070), 1.07 (95% CI: 1.053, 1.086), 1.013 (95% CI: 1.008, 1.018), 1.009 (95% CI: 1.007, 1.011), and 1.337 (95% CI: 1.234, 1.449), respectively. Notably, men, individuals aged ≥50 years, those with lower educational attainment, nonsmokers, and those not engaging in physical exercise displayed more susceptible to the adverse effects of air pollution. Multiple sensitivity analyses confirmed the stability of these findings. Our study provides robust evidence of a correlation between prolonged exposure to air pollution and the prevalence of DM among individuals living in the desert-adjacent areas. This research contributes to the expanding knowledge on the relationship between air pollution exposure and DM prevalence in desert-adjacent areas.

Gaussian process-based analysis of the nitrogen dioxide at Madrid Central Low Emission Zone

Abstract Concern about air-quality in urban areas has led to the implementation of Low Emission Zones as one of many other initiatives to control it. Recently in Spain, the enactment of a law made this mandatory for cities with a population larger than 50k inhabitants. The delimitation of these areas is not without controversy because of possible negative economic and social impacts. Therefore, clear assessments of how these initiatives decrease pollutant concentrations are to be provided. Madrid Central is a major initiative of Madrid city council for reducing motor traffic and the associated air pollution in the city centre. This Low Emission Zone starts at the end of 2018, but the first fully-operational period corresponds to the second quarter of 2019. In this work, a methodology based on the Gaussian Process to analyse the evolution of Nitrogen Dioxide inside Madrid Central is undertaken. A Gaussian Process is a stochastic process suitable for interpretable model selection and predictions. Due to its probabilistic nature it provides error estimation at predictions. After the activation of Madrid Central, a relevant reduction of Nitrogen Dioxide has been observed. However, the role of the meteorology during this period must be ascertained to correctly evaluate the role of the activation of the Low Emission Zone against a prone weather. In this work, a model based on the Gaussian Process is trained with meteorological information to predict the concentration of Nitrogen Dioxide at Madrid Central, $[NO_{2}]$. This probabilistic description allows extracting statistical information on the reduction affected by the meteorological scenario and separately by the Madrid Central activation.

A 1D CNN-Based Emulator of CMAQ: Predicting NO2 Concentration over the Most Populated Urban Regions in Texas

Abstract In this study, we developed an emulator of the Community Multiscale Air Quality (CMAQ) model by employing a one-dimensional (1D) convolutional neural network (CNN) algorithm to predict hourly surface nitrogen dioxide (NO2) concentrations over the most densely populated urban regions in Texas. The inputs for the emulator were the same as those for the CMAQ model, which includes emission, meteorological, and land-use/land-cover data. We trained the model over June, July, and August (JJA) of 2011 and 2014 and then tested it on JJA of 2017, achieving an index of agreement (IOA) of 0.95 and a correlation of 0.90. We also employed temporal threefold cross validation to evaluate the model’s performance, ensuring the robustness and generalizability of the results. To gain deeper insights and understand the factors influencing the model’s surface NO2 predictions, we conducted a Shapley additive explanations analysis. The results revealed solar radiation reaching the surface, planetary boundary layer height, and NOx (NO + NO2) emissions are key variables driving the model’s predictions. These findings highlight the emulator’s ability to capture the individual impact of each variable on the model’s NO2 predictions. Furthermore, our emulator outperformed the CMAQ model in terms of computational efficiency, being more than 900 times as fast in predicting NO2 concentrations, enabling the rapid assessment of various pollution management scenarios. This work offers a valuable resource for air pollution mitigation efforts, not just in Texas, but with appropriate regional data training, its utility could be extended to other regions and pollutants as well. Significance Statement This work develops an emulator of the Community Multiscale Air Quality model, using a one-dimensional convolutional neural network to predict hourly surface NO2 concentrations across densely populated regions in Texas. Our emulator is capable of providing rapid and highly accurate NO2 estimates, enabling it to model diverse scenarios and facilitating informed decision-making to improve public health outcomes. Notably, this model outperforms traditional methods in computational efficiency, making it a robust, time-efficient tool for air pollution mitigation efforts. The findings suggest that key variables like solar radiation, planetary boundary layer height, and NOx (NO + NO2) emissions significantly influence the model’s NO2 predictions. By adding appropriate training data, this work can be extended to other regions and other pollutants such as O3, PM2.5, and PM10, offering a powerful tool for pollution mitigation and public health improvement efforts worldwide.

Measuring the concentrations of some gaseous air pollutants and dust particles in the western city of Hamzah in Iraq

This research concentrates on studying and analyzing the spatial and temporal variation of the concentrations of air pollutants in the Hamzah-western city during the four seasons of the year 2022 by studying the concentrations of gaseous pollutants in the air, such as NO2, SO2, CO, CO2, and O3, as well as measuring the concentrations of dust particles suspended in the air, such as PM2.5 and PM10. The number of sampling sites reached six, distributed into five different sectors (industrial, residential, transport, agricultural, and a mixed sector combining industry and transportation) in order to show the discrepancies in the concentrations of pollutants and explain their causes. The results of in situ measurements and laboratory analyses showed that there is a spatial and temporal variation in the concentrations of air pollutants. The highest concentrations of carbon monoxide CO, carbon dioxide CO2, and sulfur dioxide SO2 were recorded in sector 1 and during the summer season at concentrations of 38.5, 337, and 2.9 ppm, respectively. While the highest concentrations of nitrogen dioxide (NO2) were recorded in sector (2) with a concentration of 0.65 ppm, and all those concentrations were outside the permissible limits, the lowest concentrations were recorded for gases in sector (6), which covered all the study semesters. As for the concentrations of suspended dust particles in their sizes (PM2.5 and PM10), the highest concentrations were recorded in Sector (2), during all seasons of the study, and at 383.81 and 389.38 ppm concentrations, respectively. While the lowest concentrations of suspended particles of both sizes were recorded in Sector 6, a value of 119.65 ppm was recorded for PM2.5 minutes and 115.65 ppm for PM10 minutes. Thus, all their values during the study period were greater than the permissible limits.

Impact of the No-Driving Day Program on Air Quality in a High-Altitude Tropical City: The Case of the Toluca Valley Metropolitan Area

This study addresses the pressing issue of urban air pollution impact, emphasizing the need for emissions control to ensure environmental equity. Focused on the Toluca Valley Metropolitan Area (TVMA), this research employs air quality modeling to examine ozone, sulfur dioxide, nitrogen dioxide, and carbon monoxide concentrations during three different periods in 2019. It quantitatively assesses the performance of a state-of-the-art air quality model while evaluating the efficacy of a No-Driving day mitigation measure program, similar to the one which is currently implemented in Mexico City. Using an updated national emissions inventory for 2016, this study highlights the model capability of representing ozone formation and shows that reducing mobile emissions of key pollutants contributes to lowering downwind surface ozone levels, albeit with a minimal local impact. The insights and tools from this work hold potential value for decision-making in the broader Megalopolis context, aligning with global efforts to comprehend and mitigate urban air pollution impacts.

The Influence of Ozone Depletion Potential Weighted Anthropogenic Emissions of Nitrous Oxide

The effects of anthropogenic emissions of nitrous oxide (N2O), carbon dioxide (CO2), methane (CH4), and halocarbons on stratospheric ozone (O3) over the twentieth and twenty-first centuries are divided using a chemical model of the stratosphere. As halocarbon levels revert to pre-industrial levels, N2O and CO2 will likely play the primary roles in the evolution of ozone in the future. It is unable to distinguish clearly between these gases’ effects on ozone due to nonlinear interactions between them. The work was conducted using the monthly and annual data of the gases in the stratospheric layer to determine the overlap between N2O and O3 for the Iraq station and for the period (2003-2016). The strength of the association between gases was determined using the Spearman rho test (rs). It was found that there is a very high positive relationship between the N2O and O3 in Nasriyah station, which is 0.807 which indicates a strong association. This is because meteorological conditions, climatic fluctuations, and atmospheric location all affect correlation. The ozone layer is destroyed by nitrous oxide at high concentrations through the stratosphere layer and the tropospheric layer, the concentration of ozone increases with an increase in the concentration of nitrogen dioxide.

Spatial modeling of air pollution in Kharkiv city

&#x0D; &#x0D; &#x0D; One of the most pressing problems facing cities, which negatively affects public health. There is a global practice of online monitoring of the content of pollutants in the air of settlements and informing the population about dangerous periods of increased pollution concentrations for certain categories of diseases and the population as a whole. It is also likely that one of the factors of pollution spread is the landscape differentiation of the city’s territory. The purpose of the study presented in this article is to develop statistical and cartographic models of the spatial distribution of air pollutants within the city of Kharkiv and to establish the relationship between the degree of air pollution and types of urban landscapes. Methods. Based on the analysis of the main methodological approaches and tasks of cartographic modeling of the spatial distribution of air pollution, the study substantiates the choice of the modeling method in this study; the features of a large city as an object of cartographic modeling of air pollution are assessed. Results. Based on the results of long-term monitoring by the Kharkiv Regional Center for Hydrometeorology, a statistical analysis of the results of the maximum and average content of pollutants in the atmosphere of Kharkiv was made and the spatial distribution of air pollution in Kharkiv (including (dust, carbon monoxide, nitrogen dioxide, sulfur dioxide, formaldehyde) was modeled, and patterns of pollution distribution were identified according to landscape types. Conclusions. In general, the spatial analysis of pollutant concentrations in the territories of Kharkiv showed that, in terms of maximum concentrations, the southeastern part of Kharkiv is most affected by carbon monoxide, nitrogen dioxide, and formaldehyde. The reason for this is the large area of industrial facilities. The northwestern part of the city has the highest concentrations of dust and sulfur dioxide. There is a lowering of the relief in the floodplain type of landscape and dense development. Intensive traffic and lowered relief (floodplain landscape) cause high average concentrations of dust, carbon monoxide, and sulfur dioxide in the southern part of the city. Nitrogen dioxide concentrations exceed the TLK in the northwest. The reason is the lowering of the relief on the outskirts of the city (floodplain landscape). The central part of the city has a high average concentration of formaldehyde. In general, a pattern of high concentrations of pollutants was found on the floodplains of the Lopan and Kharkiv rivers, with the lowest concentrations confined to watershed types of terrain between the Kharkiv and Lopan rivers and the Udy and Lopan rivers.&#x0D; &#x0D; &#x0D;

Chronic exposure to ambient air pollution and the risk of non-alcoholic fatty liver disease: A cross-sectional study in Taiwan and Hong Kong

BackgroundInformation on the relation of air pollution with non-alcoholic fatty liver disease (NAFLD) is scarce. We thus conducted a large cross-sectional study in Asia to investigate the role of air pollution in NAFLD. MethodsWe recruited 329,048 adults (mean age: 41.0 years) without other liver disease (hepatitis and cirrhosis) or excessive alcohol consumption in Taiwan and Hong Kong from 2001 to 2018. The concentrations of nitrogen dioxide (NO2) and ozone (O3) were estimated using a space–time regression model, and the concentrations of fine particulate matter (PM2.5) was evaluated using a satellite-based spatio-temporal model. NAFLD was determined using either the fatty liver index (FLI) or the hepatic steatosis index (HSI). The NAFLD-related advanced fibrosis was defined according to BARD score or the fibrosis-4 (FIB-4). A logistic regression model was adopted to explore the relationships of ambient air pollution with the odds of NAFLD and NAFLD-related advanced fibrosis. ResultsWe found positive relationships between PM2.5 and the odds of NAFLD and advanced fibrosis, with every standard deviation (SD, 7.5 µg/m3) increases in PM2.5 exposure being associated with a 10% (95% confidence interval [CI]: 9%–11%) increment in the prevalence of NAFLD and an 8% (95% CI: 7%–9%) increment in the prevalence of advanced fibrosis. Similarly, the prevalence of NAFLD and advanced fibrosis increased by 8% (95% CI: 7%–9%) and 7% (95% CI: 6%–8%) with per SD (18.9 µg/m3) increasement in NO2 concentration, respectively. Additionally, for every SD (9.9 µg/m3) increasement in O3 concentration, the prevalence of NAFLD and advanced fibrosis decreased by 12% (95% CI: 11%–13%) and 11% (95% CI: 9%–12%), respectively. ConclusionHigher ambient PM2.5 and NO2 are linked with higher odds of NAFLD and advanced fibrosis. Our findings indicate that reducing PM2.5 and NO2 concentrations may be an effective way for preventing NAFLD. Further studies on O3 are warranted.

Long-Term Exposure to Air Pollution and Risk of Acute Lower Respiratory Infections in the Danish Nurse Cohort.

Air pollution is a major risk factor for chronic cardiorespiratory diseases, affecting both the immune and respiratory systems' functionality, while the epidemiological evidence on respiratory infections remains sparse. We aimed to assess the association of long-term exposure to ambient air pollution with risk of developing new and recurrent ALRIs that characterized by persistently severe symptoms necessitating hospital contact, and identify the potential susceptible populations by socio-economic status (SES), smoking, physical activity status, overweight, and co-morbidity with chronic lung disease. We followed 23,912 female nurses from the Danish Nurse Cohort (> 44 years) from baseline (1993 or 1999) until 2018 for the incident and recurrent ALRIs defined by hospital contact (in-, outpatient, and emergency room) data from the National Patient Register. Residential annual mean concentrations of fine particulate matter (PM2.5), nitrogen dioxide (NO2), and black carbon (BC) were modelled using Danish DEHM/UBM/AirGIS system. We used marginal Cox models with time-varying exposures to assess the association of 3-year running-mean air pollution with incident and recurrent ALRIs and examine effect modification by age, socio-economic status (SES), smoking, physical activity, body mass index, and comorbidity with asthma or chronic obstructive pulmonary disease (COPD). During a 21.3 years mean follow-up, 4,746 ALRIs were observed, of which 2,553 were incident. We observed strong positive associations of all three pollutants with incident ALRIs, with hazard ratios and 95% confidence intervals of 1.19 (1.08-1.31) per 2.5 µg/m3 for PM2.5, 1.17 (1.11-1.24) per 8.0 µg/m3 for NO2, and 1.09 (1.05-1.12) per 0.3 µg/m3 for BC, and slightly stronger associations with recurrent ALRIs. Associations were strongest in COPD patients and nurses with low physical activity. Long-term exposure to air pollution at low levels was associated with risk of new and recurrent ALRIs, with COPD patients and physically inactive subjects most vulnerable. Primary Source of Funding: This study was supported by the Novo Nordisk Foundation Challenge Programme (NNF17OC0027812).

Spatio-temporal evaluation of air pollution using ground-based and satellite data during COVID-19 in Ecuador

The concentration of gases in the atmosphere is a topic of growing concern due to its effects on health, ecosystems etc. Its monitoring is commonly carried out through ground stations which offer high precision and temporal resolution. However, in countries with few stations, such as Ecuador, these data fail to adequately describe the spatial variability of pollutant concentrations. Remote sensing data have great potential to solve this complication. This study evaluates the spatiotemporal distribution of nitrogen dioxide (NO2) and ozone (O3) concentrations in Quito and Cuenca, using data obtained from ground-based and Sentinel-5 Precursor mission sources during the years 2019 and 2020. Moreover, a Linear Regression Model (LRM) was employed to analyze the correlation between ground-based and satellite datasets, revealing positive associations for O3 (R2 = 0.83, RMSE = 0.18) and NO2 (R2 = 0.83, RMSE = 0.25) in Quito; and O3 (R2 = 0.74, RMSE = 0.23) and NO2, (R2 = 0.73, RMSE = 0.23) for Cuenca. The agreement between ground-based and satellite datasets was analyzed by employing the intra-class correlation coefficient (ICC), reflecting good agreement between them (ICC ≥0.57); and using Bland and Altman coefficients, which showed low bias and that more than 95% of the differences are within the limits of agreement. Furthermore, the study investigated the impact of COVID-19 pandemic-related restrictions, such as social distancing and isolation, on atmospheric conditions. This was categorized into three periods for 2019 and 2020: before (from January 1st to March 15th), during (from March 16th to May 17th), and after (from March 18th to December 31st). A 51% decrease in NO2 concentrations was recorded for Cuenca, while Quito experienced a 14.7% decrease. The tropospheric column decreased by 27.3% in Cuenca and 15.1% in Quito. O3 showed an increasing trend, with tropospheric concentrations rising by 0.42% and 0.11% for Cuenca and Quito respectively, while the concentration in Cuenca decreased by 14.4%. Quito experienced an increase of 10.5%. Finally, the reduction of chemical species in the atmosphere as a consequence of mobility restrictions is highlighted. This study compared satellite and ground station data for NO2 and O3 concentrations. Despite differing units preventing data validation, it verified the Sentinel-5P satellite's effectiveness in anomaly detection. Our research's value lies in its applicability to developing countries, which may lack extensive monitoring networks, demonstrating the potential use of satellite technology in urban planning.

Application of the principles of systems analysis for the development of environment for integrated air pollution forecasting

Air pollution forecasting is a well-known scientific problem, but nitrogen dioxide is not often considered as a pollutant in the problem. Traffic, presented by vehicle emissions, is one of the main sources of air pollution by nitrogen dioxide. Some existing investigations take traffic into account as input features for forecasting models but traffic is presented by values measured during previous hours. Therefore, separate models are created and trained for every air pollution measurement station. Trained forecasting models are not used systemati-cally and models for forecasting other indicators are not applied. But air pollution over cur-rent hour is caused by traffic mostly at the same place over the same hour but not over previ-ous hours. Therefore, it is important to investigate how usage of forecasted values of traffic impacts on air pollution by nitrogen dioxide in comparison with usage of traffic values meas-ured over previous hours. This task is considered in the paper. The objective of the study is to investigate impact of system approach on accuracy of forecasting air pollution by nitrogen dioxide. Three types of forecasting models based on long short-term memory were created to investigate such an impact. These types include models with concentration of nitrogen dioxide over previous hours as input features and two types of models with additional input features presented by values of traffic over previous hours and by forecasted values of traffic over output hours correspondingly. The last type of models pre-sented application of systems approach, as it uses other models available in the integrated forecasting environment. Experimental investigation allowed to create and train models as well as to compare the results of the models on the datasets representing car traffic and air pollution in Madrid. Application of the approach based on the principles of systems analysis increases accuracy of forecasting air pollution by nitrogen dioxide. It enables to create inte-grated air pollution forecasting environment, which combines information technologies of traffic and air pollution forecasting.

City-scale methane emissions from the midstream oil and gas industry: A satellite survey of the Zhoushan archipelago

As the second most abundant greenhouse gas after carbon dioxide, methane (CH4) leakage and emissions pose potential climate threats and environmental problems in the midstream oil and gas storage and transportation (OGST) industry. In this study, the Tropospheric Monitoring Instrument on the Sentinel-5P satellite was adopted to investigate the coverage of atmospheric CH4 concentrations over the Zhoushan archipelago, which is China's largest OGST base. Specifically, the distribution of atmospheric CH4 and nitrogen dioxide (NO2) concentrations over Zhoushan in August 2022 and August 2023 were evaluated, where changes in concentrations around the OGST companies were monitored. Using surface temperature inversion and average temperature statistics, we analyzed the heat island effect in Zhoushan. The results showed that: (1) although there is limited valid data (>200 pixel/day) for Zhoushan, the amount of valid data increased from 3.2% in January 2020 to 32.3% in August 2023; (2) around the region of the OGST companies, the atmospheric CH4 concentration shown significant variation at both the spatial and temporal scales, which may be due to the storage, loading, and unloading of petroleum products; (3) under geographic isolation conditions, NO2 can be used as a tracer gas to track the CH4 emissions from OGST companies; and (4) Surface temperature analysis underscores OGST companies' contribution to Zhoushan's urban heat island effect, with local average temperature increases outstripping broader national trends, highlighting the intricate interplay of human and natural factors in the city-scale climate dynamics.

Evaluating the multi-variable influence on O3, NO2, and HCHO using BRTs and RF model

This study addresses significant knowledge gaps in understanding the complex interplay between atmospheric chemistry and synoptic conditions. Using emerging machine learning techniques—Boosted Regression Trees (BRTs) and Random Forest (RF) models—we investigate the influence of synoptic conditions on pollutant levels. Several BRTs and RF models are developed to estimate surface concentrations of ozone (O3), nitrogen dioxide (NO2), and formaldehyde (HCHO). By considering a range of algorithmic structures and explanatory variables for each pollutant, the research aims to identify the most skillful predictive approaches and influential factors governing pollutant levels. The design seeks to highlight key determinants of concentration patterns without constraining the investigation to pre-defined model structures or explanatory variable sets. Introducing a novel methodology, Correlation Coefficient Differential Evaluation (C2DE), we quantitatively assess the influence of explanatory variables. C2DE reveals significant contributions from spatial variables (i.e., trajectory clusters at varying altitudes), formaldehyde to nitrogen dioxide ratio (FNR), and meteorological parameters. Specifically, spatial variables contribute approximately 28 % to O3 concentrations, while the FNR accounts for around 5.2–9.8 % of the overall influence. For NO2 and HCHO, spatial variables contribute around 26.5 % and 32.1 %, respectively. Moreover, when considering the combined influence of meteorological parameters, these collectively explain about 45.34 %, 35.31 %, and 45.41 % of the variations in O3, NO2, and HCHO concentrations, respectively. Thus, C2DE provides valuable insights into the relative contributions of these factors, aiding in the comprehensive evaluation of air quality dynamics. This underscores the need for a multifaceted approach to comprehending and effectively addressing air pollution before devising its control strategies.