Nitrogen Dioxide Concentrations Research Articles

Vegetation Effects on Air Pollution: A Comprehensive Assessment for Two Italian Cities

The role of urban vegetation in urban air quality is usually assessed by considering only the pollutant removal capacity of the plants. This study aims to show, for the first time, the effects of vegetation on air pollutant concentrations through its effects on meteorology, separately from its biogenic emissions. It also investigates how air quality changes when only biogenic emissions are altered by using plants with different emission factors, as well as the potential effects of introducing new vegetation into urban areas. These assessments were conducted using atmospheric modelling systems currently employed for air quality forecasting and planning, configured specifically for the cities of Bologna and Milan. Simulations were performed for two representative months, July and January, to capture summer and winter conditions, respectively. The variability in air concentrations of ozone (O3), nitrogen dioxide (NO2), and particulate matter (PM10) within the municipal boundaries was assessed monthly. When evaluating the impact of future vegetation, changes in temperature, wind speed, and relative humidity were also considered. The results indicate that vegetation influences air quality more significantly through changes in meteorological conditions than through biogenic emissions. Changes in biogenic emissions result in similar behaviours in O3 and PM10 concentrations, with the latter being affected by the changes in the concentrations of secondary biogenic aerosols formed in the atmosphere. Changes in NO2 concentrations are controlled by the changes in O3 concentrations, increasing where O3 concentrations decrease, and vice versa, as expected in highly polluted areas. Meteorologically induced vegetation effects also play a predominant role in depositions, accounting for most of the changes; however, the concentrations remain high despite increased deposition rates. Therefore, understanding only the removal characteristics of vegetation is insufficient to quantify its effects on urban air pollution.

Assessing the impact of land cover on air quality parameters in Jordan: A spatiotemporal study using remote sensing and cloud computing (2019–2022)

This study aimed to analyze the spatiotemporal concentration of air pollutants in the tropospheric layer of Jordan, in the Middle East, for 2019–2022. The study utilized remotely sensed data from two satellite systems, Sentinel-5P and Landsat-9, to retrieve information about the concentration of nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO) and land use types, respectively. The Google Earth Engine (GEE) platform and JavaScript were used to produce monthly short-term average concentration maps and time series for the three pollutants. Pearson correlation analysis was performed to evaluate the performance of Sentinel-5P data against ground-based monitoring stations in estimating NO2, SO2, and CO concentration at a regional scale. Results revealed a moderate correlation, with r-values of 0.42, 0.43, and 0.40, for NO2, SO2, and CO, respectively. The spatiotemporal analysis showed a higher concentration of SO2 and NO2 in the northern and middle regions of the country, coinciding with the spatial distribution of built-up areas and the main urban centers. On a temporal scale, the highest concentration of the three pollutants was observed in the winter months for all governorates of Jordan. For instance, it was found that the highest value of NO2 was in Balqa Governorate in December 2022, 1.57 * 10^4 mol/m2. The highest average monthly SO2 values were observed in Jerash Governorate in December 2019, 7.36 * 10^4 mol/m2. CO concentrations were mainly concentrated in the western parts of the Jordan rift valley.

Seasonal variations and hydrological management regulate nutrient transport in cascade damming: Insights from carbon and nitrogen isotopes

Reservoirs around the world have significantly altered the natural transport of nutrients in rivers. However, the specific effects of the cascade damming on the migration, transformation, and environmental consequences of these nutrients remain unclear. To address this knowledge gap, we analyzed spatiotemporal variations in water chemistry, nutrient concentrations, stable isotope of dissolved inorganic carbon (δ13CDIC) and nitrate isotope (δ15N-NO3-) in seven cascade reservoirs along the Wujiang River, each characterized by different regulatory regimes. Our findings reveal that the average absolute changes in concentrations of total nitrogen (TN), total phosphorus (TP), and silicon dioxide (SiO2) during the wet season (WS, spring and summer) were 2.4, 1.4, and 1.1 times higher than those observed in the dry season (DS, autumn and winter). During the WS, the average apparent retention efficiency (*RETf) values in the Hongjiadu reservoir were 97 % for TN, 98 % for TP, and 95 % for SiO2, indicating substantial nutrient consumption in the cascading reservoirs. Conversely, during the DS, the *RETf values for TN, TP, and SiO2 were negative, suggesting notable nutrient accumulation within the reservoirs. The nutrient fluxes released downstream from the cascade reservoirs in the Wujiang River were significantly greater than the upstream inflow fluxes. These findings help demonstrate how downstream discharge across cascade reservoirs amplifies nutrient flux disparities due to dam construction. Our study enhances the understanding of how cascade dam construction impacts nutrient dynamics, supporting the optimization of reservoir operation models and advancing scientific water resource management and conservation efforts.

Chemiresistive sensor array for quantitative prediction of CO and NO2 gas concentrations in their mixture using machine learning algorithms.

Single sensors have been developed for specific gas detection in real-time environments, but their selectivity is limited by interference from other gases when considering mixtures of gases. Consequently, accurate detection of target gases in mixed gas environments is essential. Therefore, this study develops a sensor array approach to quantitatively estimate the concentration of carbon monoxide (CO) and nitrogen dioxide (NO2) gases in their binary mixture (CO and NO2). Thesensor array consists of two different sensors, developed with zinc oxide and graphene-cobalt sulfide. The sensor array wastested in the presence of 29 different proportions of the binary mixture at room temperature. Subsequently, machine learning (ML) algorithms are applied on sensor signals to estimate the concentration of gases. The ML models unfortunately exhibited inaccurate prediction when all sensor signals were considered, therefore, to improve the prediction accuracy, the sensor signals were divided into three levels based on the mixed gas concentration regime. Interestingly, the classification and regression algorithms provided good classification accuracy (85.13 ± 3.2%) and reasonable predictions of target gas concentrations at three levels. The proposed computational framework can be extended to include additional gases in mixed gases and used in various applications, including automotive, industrial, and environmental monitoring.

Insights into relationship of environmental inequalities and multimorbidity: a population-based study

BackgroundSubstantial inequalities in the overall prevalence and patterns of multimorbidity have been widely reported, but the causal mechanisms are complex and not well understood. This study aimed to identify common patterns of multimorbidity in Serbia and assess their relationship with air pollutant concentrations and water quality indicators.MethodsThis ecological study was conducted on a nationally representative sample of the Serbian population. Data were obtained from the European Health Interview (EHIS) Survey, a periodic study designed to assess population health using widely recognized standardized instruments. The study included 13,069 participants aged 15 and older, randomly selected through a multistage stratified sampling design. Multimorbidity was defined as having two or more self-reported diagnoses of chronic non-communicable diseases. Latent class analysis (LCA) was performed to identify clusters of multimorbidity. Concentrations of particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3), as well as water quality indicators, were obtained from the Serbian Environmental Protection Agency.ResultsThe overall prevalence of multimorbidity was 33.4% [32.6%—34.2%]. Six latent classes of multimorbidity were identified: Healthy, Multicondition, Cardiovascular, Metabolic syndrome, Respiratory, and Musculoskeletal. Annual increases in PM10 and SO2 concentrations, as well as daily increases in O3 concentrations, significantly raised the odds of having multimorbidity (OR = 1.02, 95% CI 1.02–1.03; OR = 1.01, 95% CI 1.00–1.02 and OR = 1.03, 95% CI 1.02–1.03, respectively). A pattern of increased risk was observed with rising levels of water contamination. Exposure to physico-chemical, microbiological and combined contamination was associated with a 3.92%, 5.17% and 5.54% higher probability, respectively, of having multiple chronic conditions. There was strong evidence that air pollutants, as well as chemical and microbial water contamination, were significantly associated with higher odds of the most common clusters of multimorbidity identified by LCA.ConclusionThere is compelling evidence of an association between multimorbidity and environmental pollution, suggesting that exposure to air pollutants and water contaminants may contribute to disease accumulation and help explain geographically and socioeconomically patterned inequalities. These findings underscore the need for extensive studies that simultaneously measure both multimorbidity and pollution to explore their complex interrelationships.

The Dynamics of Air Pollution in the Southwestern Part of the Caspian Sea Basin (Based on the Analysis of Sentinel-5 Satellite Data Utilizing the Google Earth Engine Cloud-Computing Platform)

The Caspian region represents a complex and unique system of terrestrial, coastal, and aquatic environments, marked by an exceptional landscape and biological diversity. This diversity, however, is increasingly threatened by substantial anthropogenic pressures. One notable impact of this human influence is the rising concentration of pollutants atypical for the atmosphere. Advances in science and technology now make it possible to detect certain atmospheric pollutants using remote Earth observation techniques, specifically through data from the Sentinel-5 satellite, which provides continuous insights into atmospheric contamination. This article investigates the dynamics of atmospheric pollution in the southwestern part of the Caspian Sea basin using Sentinel-5P satellite data and the cloud-computing capabilities of the Google Earth Engine (GEE) platform. The study encompasses an analysis of concentrations of seven key pollutants: nitrogen dioxide (NO2), formaldehyde (HCHO), carbon monoxide (CO), ozone (O3), sulfur dioxide (SO2), methane (CH4), and the Aerosol Index (AI). Spatial and temporal variations in pollution fields were examined for the Caspian region and the basins of the seven rivers (key areas) flowing into the Caspian Sea: Sunzha, Sulak, Ulluchay, Karachay, Atachay, Haraz, and Gorgan. The research methodology is based on the use of data from the Sentinel-5 satellite, SRTM DEM data on absolute elevations, surface temperature data, and population density data. Data processing is performed using the Google Earth Engine cloud-computing platform and the ArcGIS software suite. The main aim of this study is to evaluate the spatiotemporal variability of pollutant concentration fields in these regions from 2018 to 2023 and to identify the primary factors influencing pollution distribution. The study’s findings reveal that the Heraz and Gorgan River basins have the highest concentrations of nitrogen dioxide and Aerosol Index levels, marking these basins as the most vulnerable to atmospheric pollution among those assessed. Additionally, the Gorgan basin exhibited elevated carbon monoxide levels, while the highest ozone concentrations were detected in the Sunzha basin. Our temporal analysis demonstrated a substantial influence of the COVID-19 pandemic on pollutant dispersion patterns. Our correlation analysis identified absolute elevation as a key factor affecting pollutant distribution, particularly for carbon monoxide, ozone, and aerosol indices. Population density showed the strongest correlation with nitrogen dioxide distribution. Other pollutants exhibited more complex distribution patterns, influenced by diverse mechanisms associated with local emission sources and atmospheric dynamics.

Estimation of Near-surface NO2 Concentration in Guangdong Province Based on Catboost Model

Nitrogen oxide （NOx） is an important air pollutant in the atmosphere, and nitrogen dioxide （NO2） is one of its main components. The monitoring and estimation of NO2 concentration is very important for environmental protection and public health. The near-real-time nitrogen dioxide concentration data （NRTI NO2）, ERA5 meteorological reanalysis data, and DEM data provided by Sentinel-5P atmospheric pollution monitoring satellite were used as estimation variables to estimate the near-surface NO2 concentration in Guangdong Province based on the Catboost model. The results showed that： ① The Catboost model estimated the near-surface NO2 concentration with the highest accuracy, with the coefficient of determination （R2）, root mean square error （RMSE）, and mean absolute error （MAE） of the model fit reaching 0.91, 4.89 μg·m-3, and 3.45 μg·m-3 and the cross-validated R2, RMSE, and MAE reaching 0.90, 4.91 μg·m-3, and 3.43 μg·m-3, with good stability on the monthly and quarterly scales. ② The monthly average NO2 concentration near the surface of Guangdong Province showed a U-shaped trend, with the highest value of 43.8 μg·m-3 in January and the lowest value of 14.37 μg·m-3 in July. The seasonal distribution of the near-surface NO2 concentration was characterized by "high during winter and low during summer and transitional during spring and autumn," and the NO2 concentration in each season was in the following order： winter （27.53 μg·m-3） > spring （20.77 μg·m-3） > autumn （18.77 μg·m-3） > summer （14.85 μg·m-3）. ③ From a spatial distribution perspective, areas with high near-surface NO2 values in Guangdong Province were mainly located in rapidly developing and densely populated areas, while areas with low values were mainly distributed in areas focusing on port economy, agriculture, and new energy sources.

Периоперационное кондиционирование оксидом азота для предотвращения острого почечного повреждения при кардиохирургических вмешательствах у пациентов с хронической болезнью почек: промежуточные результаты рандомизированного контролируемого исследования DEF

INTRODUCTION: Acute kidney injury (AKI) is a common and serious complication after cardiac surgery performed under cardiopulmonary bypass (CPB). Patients with chronic kidney disease (CKD) are particularly vulnerable to cardiac surgery-associated AKI. Nitric oxide (NO) has great potential for implementing multi-organ protection in cardiac surgery. OBJECTIVES: To test the hypothesis that perioperative nitric oxide conditioning has a good safety profile and reduces the incidence of acute kidney injury during cardiac surgery under cardiopulmonary bypass in patients with chronic kidney disease. MATERIAL AND METHODS: An interim analysis of the results of the single-center prospective randomized controlled trial DEFENDER was conducted. Currently, the study included 96 patients with CKD who underwent cardiac surgery under CPB. Patients was randomized into 2 equal groups consisting of 48 people each. In the experimental group, during the operation and 6 hours after it, 80 ppm NO was delivered through the ventilator and CPB circuits. In the control group, during the operation and 6 hours after it, a standard oxygen-air mixture without NO was delivered through the ventilator and CPB circuits. The end points of the study were: the difference between groups in the incidence of AKI, the incidence of postoperative complications, outcomes and the safety profile of NO therapy. RESULTS: In the experimental group, the incidence of AKI was significantly lower compared to the control group (16,7 и 35,4 %, p = 0.036). In the experimental group, nitrogen dioxide concentration in the gas-air mixture and methemoglobin level were not above the reference range. The groups did not differ in the frequency of blood transfusions, the amount of postoperative bleeding, or the number of platelets on the first day after surgery. CONCLUSIONS: Perioperative nitric oxide conditioning has a good safety profile and reduces the incidence of acute kidney injury during cardiac surgery with CPB in patients with chronic kidney disease.

AIR QUALITY IN YEREVAN IN THE CONTEXT OF CLIMATE CHANGE

Over the past five years, the atmospheric air quality in Yerevan has continued to deteriorate, primarily attributed to a significant increase in airborne dust, particularly during the hot summer months that coincide with the peak tourist season. This phenomenon reflects a complex interplay among atmospheric emissions, air quality, emission source altitudes, changes in chemical composition, solar radiation, weather patterns, and topographic conditions. Sources of dust pollution in Yerevan include industrial activities, vehicular emissions, road dust, and construction activities. Data from various administrative districts indicate that, over the past five years, concentrations of dust, nitrogen dioxide, and sulfur dioxide frequently exceed established standards. Additionally, there are unregulated static sources of dust pollution within the city. This study conducts a comparative analysis of the primary pollutants in the context of climate change and urbanization in Yerevan over the last five years. It also assesses green areas and evaluates the effectiveness of greening initiatives, while considering compliance with national legislation in the Republic of Armenia.

Determining the Effectiveness of Interventions for the Reduction of Child Exposure to Traffic-Related Air Pollution at Schools in England

Traffic-related air pollution (TRAP) is a significant risk to human health and is particularly damaging to children as a vulnerable group. TRAP exposure near schools and on the school commute is linked to a growing number of adverse health effects, including respiratory and cardiovascular disease and can lead to (and exacerbate existing) respiratory conditions. The current study aimed to assess the effectiveness of interventions for the reduction of potential child exposure to TRAP at the school gates and on the school commute. This study employed dispersion modelling to assess the effects of interventions for reducing TRAP concentrations in the vicinity of five schools in England. The results revealed that all interventions led to reductions in nitrogen dioxide (NO2) concentrations. Improved travel routes were the most effective intervention for reducing concentrations along travel routes, while the introduction of low-emission zones (LEZs) proved most effective in reducing NO2 concentrations at schools, with greater effectiveness observed at shorter distances. Active travel also demonstrated effectiveness, particularly in areas with heavy traffic. When considering all receptors, LEZ implementation, active travel, and rideshare interventions exhibited effectiveness, with greater distance providing greater reductions in NO2 concentrations. Anti-idling was found to be more effective in sparsely populated areas. Combined with improved travel routes, anti-idling showed the greatest percentage difference in concentrations, followed by active travel, and rideshare.

Applying similar modeling to study vehicle exhaust transport in a confined mining space

To scientifically and effectively manage exhaust pollution in underground confined spaces and reduce hazards from personnel exhaust exposure, a systematic study of the distribution characteristics of diesel locomotive exhaust emissions in mines is needed. Aiming at large underground coal mining operations, an experimental platform for the physical simulation of vehicle exhaust transport in restricted spaces is designed and created. The Hongliulin Coal Mine in Shaanxi Province, China, is used as a prototype, and the platform consists of a coal mining face system, ventilation system, exhaust emission system, monitoring and surveillance system, and control platform. The experimental platform with carbon dioxide simulation of diesel exhaust pollution, in the role of different wind speed, the concentration of carbon dioxide is a downward trend, the wind speed is in the low wind speed on the 0.2 m of the concentration difference of 25.75%; the experimental platform exhaust transport process presents a clear “three-zone” change rule; experimental platform carbon dioxide concentration and the field nitrogen dioxide concentration change trend is consistent. The platform functions and features can provide guidance for mine exhaust management and exhaust exposure hazard prevention and control.

Effect of Air Pollutants and Environmental Noise on the Childhood Asthma Prevalence in Tehran, Iran.

The purpose of this study is to investigate the effect of air pollutants and noise on the prevalence of childhood asthma in Tehran, Iran. The standardized questionnaire was completed by one of the parents of children aged 6-7 years or by adolescents aged 13-14 years. The asthma prevalence in ages 6-7 and 13-14 was found to be 8.8% and 17.44%, respectively. A significant positive association was observed between "ever wheezing" and monoxide carbon (CO) concentration (OR=1.84, 1.05-3.25 in 13-14 years), the occurrence of 4 to 12 wheezing attacks and sulfur dioxide (SO2) concentration (Odds Ratio (OR)=1.39, 1.04-1.91) and particulate matter less than 2.5 micron (PM2.5) concentration (OR=1.38, 1.05-1.98 and OR=1.13, 0.98-1.39 in 6-7 and 13-14 years, respectively), as well as one night per week of sleep disturbances and nitrogen dioxide (NO2) concentration (OR=1.09, 1.03-1.16 in 6-7 years, respectively). It was also found that there was a significant interaction between the noise level and particulate matter less than 10 micron (PM10) level. Based on the findings, exposure to certain outdoor air pollutants and noise can affect prevalence of asthma symptoms in residence of Tehran. The simultaneous presence of air pollutants and noise has an aggravating effect on the prevalence of asthma symptoms. Therefore, controlling sources of pollutants for reducing asthma symptoms is suggested.

Identification of optimal locations for green space initiatives through GIS-based multi-criteria analysis and the analytical hierarchy process

Urban green spaces play a vital role in enhancing the well-being of communities and mitigating environmental challenges such as air pollution and global warming. Despite their importance, effective models to allocate these green spaces are often overlooked, particularly in developing countries. This study utilises GIS-based Multi-Criteria Analysis and the Analytical Hierarchy Process to recommend optimal locations for green space interventions in Lilongwe City, Malawi, based on nine factors: population density, proximity to roads, slope, Digital Elevation Model (DEM), Normalized Difference Vegetative Index (NDVI), land cover, existing green space, proximity to water bodies, and nitrogen dioxide concentration. The results show that 0.57% (23,776 hectares) of Lilongwe city is highly suitable while 14.50% (604,596 hectares) is unsuitable for green space interventions, where population density was the most determining factor. The suitability varied across the city, with highly suitable areas predominantly located in the southern part. The study highlights the importance of informed decision-making in urban green space planning, setting a standard for equitable access to green spaces and sustainable urban development.

Impact of net zero policy scenarios on air pollution inequalities in England and Wales

BackgroundThe UK is committed to achieve net zero greenhouse gas emissions by 2050. The suite of policies needed to reach net zero will lead to improvements in air quality and, consequently, could lessen air pollution inequalities. We assessed air pollution inequalities across different sociodemographic groups in England and Wales and explored how these might be differentially impacted by future air pollution projections in 2030 and 2040 under net zero policies. MethodsWe employed a geodemographic classification approach to categorise neighbourhoods into five distinct clusters based on 2021 UK Census sociodemographic variables. We modelled fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations for the year 2019, and predicted concentrations in 2030 and 2040. We compared a business-as-usual (BAU) scenario and two policy pathways to achieve net zero currently considered by the UK government. We aggregated air pollution concentrations to the neighbourhood level and assessed differential neighbourhood-level concentrations across the geodemographic groups using descriptive statistics and box plots. ResultsThe Urban Central Professionals group experienced 14 µg/m3 higher average NO2 concentrations compared with the Rural Elderly group in 2019. Despite substantial improvements to air quality in 2030 and 2040 of up to 6.3 µg/m3 for NO2 based on BAU, and further reductions of up to 2.4 µg/m3 NO2 under net zero policies, the overall pattern of inequality persists, but is predicted to be less pronounced. ConclusionsOur findings demonstrate the effectiveness of targeted policies and innovations in reducing both air quality and greenhouse gas emissions and in bridging the environmental inequality gap. Our findings are essential to develop targeted communication campaigns to secure acceptance and willingness across the sociodemographic spectrum to support the significant behavioural changes needed to achieve net zero, by highlighting the wider co-benefits to the environment and health of such policies.

Ambient smoke exposure and indoor air quality in eastern Massachusetts during the 2023 wildfire season

ABSTRACT Widespread North American wildfires in 2023 led to exposure to ambient wildfire smoke outside of traditionally wildfire-prone regions. The objective was to evaluate levels of indoor air pollutants in relation to ambient wildfire smoke exposure in eastern Massachusetts. Using a real-time multipollutant sensor system in five Boston area households, this study assessed indoor fine particulate matter (PM2.5), nitrogen dioxide (NO2), and total volatile organic compound concentrations (TVOC) two days before and during days of hazardous wildfire smoke exposure (smoke days). The relationship between ambient PM2.5 from regulatory monitors and indoor PM2.5 before and during smoke days was investigated by mixed effects linear regression. During smoke days and the preceding non-smoke days, median indoor PM2.5 was 9.9 µg/m3 and 3.5 µg/m3 (p < 0.001), respectively; median NO2 was 20.5 ppb and 18.4 ppb (p = 0.11); median TVOC was 6,715 µg/m3 and 5,361 µg/m3 (p = 0.35). A 1% increase in ambient PM2.5 was associated with a 0.93% increase in indoor PM2.5 on smoke days (95% CI, 0.54%–1.32%) and a 0.34% increase on non-smoke days (95% CI, 0.17%–0.66%), though interaction testing of smoke day status was not statistically significant (p = 0.14). In Northeastern US homes, indoor PM2.5 increased significantly during ambient wildfire smoke exposure, which may reflect increased infiltration and increased indoor particle-generating activities during smoke days. Implications: This study reports on household exposure to wildfire smoke in eastern Massachusetts, finding that indoor PM2.5 more than doubled compared to preceding non-smoke days, while indoor NO2 and TVOC did not significantly rise. Though the generalizability of this study is limited by the small number of homes studied, the findings suggest that more investigation is needed to understand indoor air pollution during future wildfire smoke exposure in regions not traditionally wildfire-prone and to inform mitigation efforts.

Munich’s selective diesel vehicle ban and its impact on nitrogen dioxide concentrations: A quasi-experimental study

BackgroundThe current limit on NO2 concentrations of 40 μg/m3, set by the European Union, has been regularly exceeded in Munich, Germany. This limit will likely be reduced towards the WHO recommended target of 10 μg/m3. Against this backdrop, the city implemented a selective diesel vehicle ban within the existing low-emission zone in February 2023, targeting Euro 4 and older diesel vehicles. Our study investigated the effect of Munich’s selective diesel vehicle ban on NO2 concentrations, focusing on the half-year period following its implementation. MethodsOur study utilized a synthetic control approach (primary analysis) and a controlled interrupted time series approach (secondary analysis). These quasi-experimental methodologies create a ‘counterfactual’ no-intervention scenario, enabling comparison between observed and counterfactual scenarios to estimate an intervention effect. We employed historical controls, using routine data from multiple monitoring stations located within and outside the low-emission zone for 2014 to 2022, and considered possible confounders. ResultsNO2 concentrations within Munich’s low-emission zone showed overall declining trends from August 2014 to July 2023. Effects of the selective diesel vehicle ban were small and wide confidence intervals indicate large uncertainty in the magnitude and direction of the effect. At Landshuter Allee, the average intervention effect was −2.67 μg/m3 (95 %-CI = [-12.72; 7.38]), at Stachus it was −2.74 μg/m3 (95 %-CI = [-9.91; 4.42]), and at Lothstrasse it was −1.03 μg/m3 (95 %-CI = [-7.75; 5.69]). The secondary analysis confirmed these findings, reinforcing uncertainty about the effect of the intervention. ConclusionOur study suggests that Munich’s selective diesel vehicle ban had a limited effect on lowering NO2 concentrations. Possible explanations include the ban’s focus on Euro 4 and older diesel vehicles, many exemptions to the selective ban, and unclear enforcement. This highlights that comprehensive approaches and ongoing, well-designed monitoring and evaluation are crucial for addressing urban air pollution and protecting public health.

Assessment of the risk to the health of the population living in the zone influenced by atmospheric emissions from the engineering industry

Introduction. During operation, machine-building enterprises emit pollutants that can negatively affect the health of the population. The study aims to determine the impact of emissions from a machine–building plant, as well as background atmospheric pollution, on the health risk of the population of Novokuznetsk. Materials and methods. The work used data from the volume of maximum permissible emissions of a machine-building plant. The researchers calculated the maximum and average concentrations of pollutants using the "ECOcenter – Standard" program at 36 exposure points selected on the city map. Experts determined the maximum permissible concentrations of pollutants in the atmosphere in accordance with SanPiN 1.2.3685-21. The authors calculated the risks to public health in accordance with the Guidelines R 2.1.10.1920-04. The classification of risk levels was carried out on the basis of MP 2.1.10.0156-19. 2.1.10. Scientists also made a risk assessment considering background atmospheric pollution. Results. Specialists carried out a risk assessment from exposure to nitrogen dioxide, carbon monoxide, sulfur dioxide, carbon (soot), inorganic dust, hexavalent chromium. The maximum and average concentrations of pollutants, as well as the maximum concentrations calculated taking into account background pollution, did not exceed the maximum permissible level. The average concentrations of nitrogen dioxide and hexavalent chromium, taking into account the background, exceeded the hygienic standard. The values of the hazard indices for acute and chronic exposures without taking into account the background, as well as for acute exposures taking into account the background, corresponded to the minimum (target) risk level. The values of the hazard indices for chronic inhalation effects, considering the background, corresponded to the alarming risk level at five points, amounting to 3.084–3.800. With chronic exposure, taking into account the background, an alarming level of risk was revealed for the effect on the respiratory organs (at four points the hazard index was 3.117 to 3.616) and blood (at three points the hazard index was in the range from 3.090 to 3.433). The total individual carcinogenic risk, excluding background, did not exceed the permissible level, ranging from 1.08×10–8 to 2.43×10–7 at different points, taking into account the background, amounted to 1.79×10–4–2.26×10–4, which corresponds to an alarming level. Hexavalent chromium makes the main contribution to the formation of carcinogenic risk. Limitations. Calculated concentrations of pollutants were used to assess the risks. Conclusion. The risks from exposure to atmospheric emissions from the machine-building plant correspond to the minimum (target) level. Background concentrations of toxic substances in chronic inhalation exposures determine an alarming level of non-carcinogenic risk at five points, as well as an alarming level of carcinogenic risk at all points of exposure. Ethics. This study did not require the conclusion of the Ethics Committee.

Air Quality Geospatial Analysis in Vulnerable Areas. Case Study of Valencia (Spain).

The escalating concern over poor air quality, particularly nitrogen dioxide (NO2), poses a critical public health challenge, especially for vulnerable populations, such as children, older adults, and those with chronic diseases. This study aimed to analyze air quality in areas with vulnerable populations through a geospatial analysis of NO2 concentration measured by the passive dosimetry method in 2022. The results reveal high vulnerability caused by areas with over-centralized facilities and high concentrations of nitrogen dioxide, often coinciding with busy avenues. The study emphasizes the urgent need to address air quality disparities, providing crucial insights for public decision-makers to allocate resources effectively and reduce environmental inequalities in the city, ultimately safeguarding the health of at-risk communities.

Spatial Analysis of Air Pollutants in an Industrial City Using GIS-Based Techniques: A Case Study of Pavlodar, Kazakhstan

The given research employs high-resolution air quality monitoring and contemporary statistical methods to address gaps in understanding the urban air pollution in Pavlodar, a city with a significant industrial presence and promising touristic potential. Using mobile air quality sensors for detailed spatial data collection, the research aims to quantify concentrations of particulate matter (PM2.5, PM10), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ground-level ozone (O3); assess their distribution; and identify key influencing factors. In this study, we employed Geographic Information Systems (GISs) for spatial analysis, integrating multi-level B-spline interpolation to model spatial variability. Correlation analysis and structural equation modeling were utilized to explore the relationships between variables, while regression analysis was conducted to quantify these relationships. These techniques were crucial for accurately mapping and interpreting spatial patterns and their underlying factors. The study identifies PM2.5 and NO2 as the primary contributors to air pollution in Pavlodar, with NO2 exceeding the 24 h threshold in 87.38% of locations and PM2.5 showing the highest individual air quality index (AQI) in 75.7% of cases. Correlation analysis reveals a positive association between PM2.5 and AQI and a negative correlation between NO2 and AQI, likely due to the dominant influence of PM2.5 in AQI calculations. Structural equation modeling (SEM) further underscores PM2.5 as the most significant impactor on AQI, while NO2 shows no significant direct impact. Humidity is positively correlated with AQI, though this relationship is context-specific to seasonal patterns observed in May. The sectoral analysis of landscape indices reveals weak correlations between the green space ratio (GSR) and air quality, indicating that while vegetation reduces pollutants, its impact is minimal due to urban planting density. The road ratio (RR) lacks sufficient statistical evidence to draw conclusions about its effect on air quality, possibly due to the methodology used. Spatial variability in pollutant concentrations is evident, with increasing PM2.5, PM10, and AQI towards the east-northeast, likely influenced by industrial activities and prevailing wind patterns. In contrast, NO2 pollution does not show a clear geographic pattern, indicating vehicular emissions as its primary source. Spatial interpolation highlights pollution hotspots near industrial zones, posing health risks to vulnerable populations. While the city’s overall AQI is considered “moderate”, the study highlights the necessity of implementing measures to improve air quality in Pavlodar. This will not only enhance the city’s attractiveness to tourists but also support its sustainable development as an industrial center.

Evaluating background and local contributions and identifying traffic-related pollutant hotspots: insights from Google Air View mobile monitoring in Dublin, Ireland

Mobile monitoring provides high-resolution observation on temporal and spatial scales compared to traditional fixed-site measurement. This study demonstrates the use of high spatio-temporal resolution of air pollution data collected by Google Air View vehicles to identify hotspots and assess compliance with WHO Air Quality Guidelines (AQGs) in Dublin City. The mobile monitoring was conducted during weekdays, typically from 7:00 to 19:00, between 6 May 2021 and 6 May 2022. One-second data were aggregated to 377,113 8 s road segments, and 8 s rolling medians were aggregated to hourly and daily levels for further analysis. We assessed the temporal variability of fine particulate matter (PM2.5), nitrogen monoxide (NO), nitrogen dioxide (NO2), ozone (O3), carbon monoxide (CO), and carbon dioxide (CO2) concentrations at hyperlocal levels. The average daytime median concentrations of NO2 (28.4 ± 15.7 µg/m3) and PM2.5 (7.6 ± 4.7 µg/m3) exceeded the WHO twenty-four hours (24 h) Air Quality Guidelines in 49.4% and 9% of the 1-year sampling time, respectively. For the diurnal variation of measured pollutants, the morning (8:00) and early evening (18:00) showed higher concentrations for NO2 and PM2.5, mostly happening in the winter season, while the afternoon is the least polluted time except for O3. The low-percentile approach along with 1-h and daytime minima method allowed for decomposing pollutant time series into the background and local contributions. Background contributions for NO2 and PM2.5 changed along with the seasonal variation. Local contributions for PM2.5 changed slightly; however, NO2 showed significant diurnal and seasonal variability related to traffic emissions. Short-lived event enhancement (1 min to 1 h) accounts for 36.0–40.6% and 20.8–42.2% of the total concentration for NO2 and PM2.5. The highly polluted days account for 56.3% of total NO2, highlighting local traffic is the dominant contributor to short-term NO2 concentrations. The longer-lived events (> 8 h) enhancement accounts for 25% of the monitored concentrations. Additionally, conducting optimal hotspot analysis enables mapping the spatial distribution of “hot” spots for PM2.5 and NO2 on highly polluted days. Overall, this investigation suggests both background and local emissions contribute to PM2.5 and NO2 pollution in urban areas and emphasize the urgent need for mitigating NO2 from traffic pollution in Dublin.