Air Quality Monitoring Network Research Articles

Carbon based sensors for air quality monitoring networks; middle east perspective

IoT-based Sensors networks play a pivotal role in improving air quality monitoring in the Middle East. They provide real-time data, enabling precise tracking of pollution trends, informed decision-making, and increased public awareness. Air quality and dust pollution in the Middle East region may leads to various health issues, particularly among vulnerable populations. IoT-based Sensors networks help mitigate health risks by offering timely and accurate air quality data. Air pollution affects not only human health but also the region’s ecosystems and contributes to climate change. The economic implications of deteriorated air quality include healthcare costs and decreased productivity, underscore the need for effective monitoring and mitigation. IoT-based data can guide policymakers to align with Sustainable Development Goals (SDGs) related to health, clean water, and climate action. The conventional monitor based standard air quality instruments provide limited spatial coverage so there is strong need to continue research integrated with low-cost sensor technologies to make air quality monitoring more accessible, even in resource-constrained regions. IoT-based Sensors networks monitoring helps in understanding these environmental impacts. Among these IoT-based Sensors networks, sensors are of vital importance. With the evolution of sensors technologies, different types of sensors materials are available. Among this carbon based sensors are widely used for air quality monitoring. Carbon nanomaterial-based sensors (CNS) and carbon nanotubes (CNTs) as adsorbents exhibit unique capabilities in the measurement of air pollutants. These sensors are used to detect gaseous pollutants that includes oxides of nitrogen and Sulphur, and ozone, and volatile organic compounds (VOCs). This study provides comprehensive review of integration of carbon nanomaterials based sensors in IoT based network for better air quality monitoring and exploring the potential of machine learning and artificial intelligence for advanced data analysis, pollution source identification, integration of satellite and ground-based networks and future forecasting to design effective mitigation strategies. By prioritizing these recommendations, the Middle East and other regions, can further leverage IoT-based systems to improve air quality monitoring, safeguard public health, protect the environment, and contribute to sustainable development in the region.

Concept Drift Mitigation in Low-Cost Air Quality Monitoring Networks.

Future air quality monitoring networks will integrate fleets of low-cost gas and particulate matter sensors that are calibrated using machine learning techniques. Unfortunately, it is well known that concept drift is one of the primary causes of data quality loss in machine learning application operational scenarios. The present study focuses on addressing the calibration model update of low-cost NO2 sensors once they are triggered by a concept drift detector. It also defines which data are the most appropriate to use in the model updating process to gain compliance with the relative expanded uncertainty (REU) limits established by the European Directive. As the examined methodologies, the general/global and the importance weighting calibration models were applied for concept drift effects mitigation. Overall, for all the devices under test, the experimental results show the inadequacy of both models when performed independently. On the other hand, the results from the application of both models through a stacking ensemble strategy were able to extend the temporal validity of the used calibration model by three weeks at least for all the sensor devices under test. Thus, the usefulness of the whole information content gathered throughout the original co-location process was maximized.

Quantitative analysis of air pollution levels and its health implications in Delhi-NCR: A longitudinal study

Air pollution is a pressing global issue, particularly acute in rapidly urbanizing regions like Delhi-NCR. This research presents a longitudinal study to quantitatively analyze air pollution levels and their health implications in Delhi-NCR. The research objectives include assessing air quality trends, examining the relationships between various pollutants, and investigating the impact of air pollution on respiratory admissions. Data was sourced from the Ambient Air Quality Monitoring Network, covering the years 2020 to 2022, focusing on key pollutants, including PM2.5, PM10, NO2, SO2, CO, and O3. A range of data analysis techniques, including correlation analysis, regression analysis, longitudinal data analysis, spatial analysis, and compliance with WHO guidelines, were applied. Key findings highlight persistently high levels of air pollutants, strong interrelations among pollutants, and consistent respiratory admissions, especially in winter. Regression analysis confirms a significant link between PM2.5 and respiratory admissions. Longitudinal analysis demonstrates the long-term health consequences of air pollution, and spatial analysis reveals district-wise variations in air quality. The study also emphasizes seasonal strategies, compliance with international guidelines, and socioeconomic disparities in health outcomes. This research has broader implications for evidence-based policymaking, emphasizing the urgency of addressing air quality concerns. It calls for targeted interventions, district-level strategies, season-specific measures, and equitable access to healthcare. These findings offer a comprehensive basis for addressing the air quality crisis in Delhi-NCR, ultimately promoting the well-being of its residents.

Indication Variability of the Particulate Matter Sensors Dependent on Their Location.

Particulate matter (PM) suspended in the air significantly impacts human health. Those of anthropogenic origin are particularly hazardous. Poland is one of the countries where the air quality during the heating season is the worst in Europe. Air quality in small towns and villages far from state monitoring stations is often much worse than in larger cities where they are located. Their residents inhale the air containing smoke produced mainly by coal-fired stoves. In the frame of this project, an air quality monitoring network was built. It comprises low-cost PMS7003 PM sensors and ESP8266 microcontrollers with integrated Wi-Fi communication modules. This article presents research results on the influence of the PM sensor location on their indications. It has been shown that the indications from sensors several dozen meters away from each other can differ by up to tenfold, depending on weather conditions and the source of smoke. Therefore, measurements performed by a network of sensors, even of worse quality, are much more representative than those conducted in one spot. The results also indicated the method of detecting a sudden increase in air pollutants. In the case of smokiness, the difference between the mean and median indications of the PM sensor increases even up to 400 µg/m3 over a 5 min time window. Information from this comparison suggests a sudden deterioration in air quality and can allow for quick intervention to protect people's health. This method can be used in protection systems where fast detection of anomalies is necessary.

ARPALData: an R package for retrieving and analyzing air quality and weather data from ARPA Lombardia (Italy)

AbstractWe present ARPALData, an package that can help international users retrieve, handle, and analyze air quality and weather data in the Lombardy region (Northern Italy). The software provides a user-friendly tool that directly inquires into the platform of the regional environmental protection agency and ensures real-time updating of information using standardized syntax. The software provides data in standard statistical formats. Eventually, all measurements, metadata, and subsequent analytical tools are provided to users in English, facilitating accessibility to international and domestic users. Data are collected from the open database of the Regional Agency for Environmental Protection of Lombardy, namely ARPA Lombardia. ARPALData returns measurements at several temporal frequencies (infra-hourly to yearly) collected through air quality and weather ground monitoring networks managed by ARPA Lombardia, as well as estimates of several pollutants at the municipal level. In addition to data download functions, ARPALData provides functions to explore, describe, analyze, and graphically represent air quality and weather data. In particular, users are provided with functions to compute key descriptive statistics and input data maps, temporally aggregate measurements, detect outliers, and study missing-value (gap length) patterns. Herein, we discuss purposes, goals, and functioning of the package, and present three guided examples and case studies in which the software is used to characterize air quality and meteorology in different settings. The examples are designed to provide a step-by-step guide for accomplished analyses using the most relevant tools included in ARPALData.

Air pollutant emissions and sources in Lao People’s Democratic Republic: a provincial scale analysis for years 2013-2019

Recent rapid economic development in Lao People’s Democratic Republic (PDR) has increased national fuel consumption, vehicle fleet, industrial output, waste generation, and agricultural production. This has contributed to national average ambient fine particulate matter (PM2.5) air pollutant levels that are four times higher than World Health Organisation guidelines. Emission inventories are a key tool in understanding the major sources to these air pollution levels, and provide a starting point to identify where mitigation action can be targeted. A national air pollutant emission inventory has not been developed in Lao PDR and, combined with a limited air quality monitoring network means there is limited capacity to develop and track the effectiveness of mitigation actions. This study describes the first air pollutant emission inventory at the national and provincial scale for Lao PDR, covering 2013–2019. Emissions of nine air pollutants, and two greenhouse gases, were quantified using national statistics and international default emission factors. In 2019, national total PM2.5, Nitrogen Oxides (NOx), Black Carbon (BC), Sulphur Dioxide (SO2), Non-Methane Volatile Organic Carbons (NMVOCs), and Ammonia (NH3) were 125, 83, 9.7, 26, 219, and 99 thousand tonnes respectively. Key source sectors include forest fires, residential cooking, agriculture, electricity generation, and transport. However, the contribution of different sources varies across provinces. Forest fires are the primary source determining the spatial trend of particulate air pollution while residential and agricultural emissions contribute more significantly to rural provinces such as Savannakhet. Key sectors in major urban provinces (Vientiane Capital and Xayaboury) are industry, transport and electricity generation. These sectors are also significant sources of greenhouse gases (CO2 and CH4), demonstrating the potential for identification, evaluation and prioritisation of actions that simultaneously improve air quality and achieve Lao PDR’s international climate change commitments.

AIR QUALITY AND PUBLIC HEALTH: A REVIEW OF URBAN POLLUTION SOURCES AND MITIGATION MEASURES

Urbanization has led to unprecedented challenges in maintaining air quality, significantly impacting public health. This review delves into the diverse sources of urban pollution and explores effective mitigation measures to safeguard public well-being. The rising concentration of pollutants in urban areas, primarily attributed to industrial activities, vehicular emissions, and domestic energy consumption, poses a severe threat to the respiratory and cardiovascular health of urban populations. The review begins by examining the key sources of urban pollution, highlighting the complex interplay between anthropogenic activities and their cumulative impact on air quality. Industrial emissions, characterized by the release of particulate matter, volatile organic compounds, and hazardous chemicals, contribute significantly to urban air pollution. Likewise, the surge in vehicular traffic, fueled by rapid urbanization, introduces pollutants such as nitrogen oxides and carbon monoxide, further exacerbating the problem. To address these challenges, the review explores a spectrum of mitigation measures. Implementation of stringent emission standards for industries, adoption of cleaner technologies, and promotion of renewable energy sources emerge as critical strategies. Additionally, urban planning initiatives focusing on efficient public transportation systems, pedestrian-friendly infrastructure, and green spaces are examined as effective means to reduce vehicular emissions and enhance air quality. The review also underscores the importance of public awareness campaigns and community engagement in fostering a collective commitment to air quality improvement. Innovative technological solutions, such as air quality monitoring networks and real-time data analytics, are discussed as indispensable tools for informed decision-making and prompt intervention. This review synthesizes current knowledge on urban pollution sources and mitigation measures, emphasizing the urgent need for a multidimensional approach to protect public health. By comprehensively addressing the intricate web of pollution contributors and implementing proactive measures, urban environments can be transformed into healthier and sustainable spaces for all residents.
 Keywords: Air Quality, Public Health, Urban Pollution, Mitigation, Review.

Wildfires exacerbate inequalities in indoor pollution exposure

Wildfires lead to dramatic increases in fine particulate matter pollution concentrations. Based on the premise that higher-income households purchase more defensive investments to reduce the degree to which outdoor pollution infiltrates indoors, in this study, we investigate how income contributes to outdoor–indoor pollution infiltration rates during wildfire events. Using crowd-sourced data from the PurpleAir Real-Time Air Quality Monitoring Network and econometric models that explore variations in monitor readings over time, we find increases in outdoor pollution lead to significant increases in indoor pollution, but disproportionately so in lower-income areas. The results highlight a new inequality in pollution exposure: not only are outdoor pollution levels higher for lower-income individuals, but indoor pollution levels are higher even for similar outdoor pollution levels.

Assessment of environmental risk areas based on airborne pollen patterns as a response to land use and land cover distribution

Allergic respiratory diseases are considered to be among the most important public health concerns, and pollen is the main cause of allergic respiratory diseases worldwide. However, the biological component of air quality is largely underestimated, and there is an important gap in the legislation in this area. The aims of this study were to characterise the occurrence and incidence of pollen exposure in relation to potential pollen sources and to delineate the main areas of aerobiological risk in the Madrid Autonomous Region based on homogeneous patterns of pollen exposure. This study uses the historical aerobiological database of the Madrid Region Palynological Network (central Spain) from ten pollen stations from 1994 to 2022, and the land-use information from the Corine Land Cover. Multiple clustering approaches were followed to group the sampling stations and subsequently all the 1 × 1km pixels for the Madrid Autonomous Region. The clustering dendrogram for land-use distribution was compared to the dendrogram for historical airborne pollen data. The two dendrograms showed a good alignment with a very high correlation (0.95) and very low entanglement (0.15), which indicates a close correspondence between the distribution of the potential pollen sources and the airborne pollen dynamics. Based on this knowledge, the Madrid Autonomous Region was divided into six aerobiological risk areas following a clear anthropogenic gradient in terms of the potential pollen sources that determine pollen exposure in the Madrid Region. Spatial regionalisation is a common practice in environmental risk assessment to improve the application of management plans and optimise the air quality monitoring networks. The risk areas proposed by scientific criteria in the Madrid Autonomous Region can be adjusted to other operational criteria following a framework equivalent to other air quality networks.

Assessing the Precision of Radon Measurements from Beta-Attenuation Monitors

Atmospheric radon measurements assist in many aspects of climate and meteorological research, notably as an airmass tracer and for modelling boundary layer development, mixing heights and stability. Daughter products from radon decay are sometimes incorporated into the particle pollution measurements of commercially available beta-attenuation monitors (BAM). BAMs incorporating radon measurements are used in air quality monitoring networks and can supplement traditional radon measurements. Here we compare in-situ radon measurements from Thermo Fisher Scientific (Franklin, MA, USA) BAM instruments (Thermo Scientific 5014i, Thermo Scientific 5030 SHARP, Thermo Anderson FH62C14) at two air quality monitoring stations in New South Wales, Australia. Between systems we find strong correlations for hourly measurements (r = 0.97–0.99); daily means (r = 0.97–0.99); hour of the day (r = 0.84–0.98); and month (r = 0.82–0.98). The regression analysis for radon measurements between systems showed strong linear responses, although there are some variations in the slopes of the regressions. This implies that with correction BAM measurements can be comparable to standard measurement techniques, for example, from the Australian Nuclear Science and Technology Organisation (ANSTO) dual flow loop monitors. Our findings imply that BAM derived radon measurements are precise, although their accuracy varies. BAM radon measurements can support studies on boundary layer development or where radon is used as an atmospheric transport tracer.

The association between ambient air pollution exposure and connective tissue sarcoma risk: a nested case-control study using a nationwide population-based database.

A nationwide population-based database was utilized in a nested case-control study to explore the association between ambient air pollution exposure and the likelihood of developing connective tissue sarcoma. The study examined 280 cases of connective tissue sarcoma diagnosed between 2000 and 2012. A random sample of 1120 control subjects was selected from a subpopulation of claim records without a connective tissue sarcoma diagnosis in a 1:4 ratio. The control subjects were selected based on similar characteristics as the connective tissue sarcoma patients, including gender, birth year, and the year of diagnosis of the case group with medical records. Risk factors for connective tissue sarcoma were collected for analysis. Our data on exposure to air pollutants was collected from Taiwan's Air Quality Monitoring Network, which has been gathering air quality data from a growing network of sampling stations (now 76) throughout the country since 1997. It was discovered that the risk of connective tissue sarcoma was significantly increased by the Charlson comorbidity index (CCI), elevated levels of specific air pollution indices (e.g., total hydrocarbons (THC), fine particulate matter (PM2.5), and O3_8 (the annual mean of the daily maximum 8-h average concentration of O3), the High Pollutant Standards Index (hPSI) (the percentage of days in a given year in Taiwan where the PSI exceeds 100), and an insurable monthly wage over US$1100. Further investigation is needed to explore the involvement of these air pollutants in the formation of connective tissue sarcoma.

Observational studies generate misleading results about the health effects of air pollution: Evidence from chronic air pollution and COVID-19 outcomes.

Several observational studies from locations around the globe have documented a positive correlation between air pollution and the severity of COVID-19 disease. Observational studies cannot identify the causal link between air quality and the severity of COVID-19 outcomes, and these studies face three key identification challenges: 1) air pollution is not randomly distributed across geographies; 2) air-quality monitoring networks are sparse spatially; and 3) defensive behaviors to mediate exposure to air pollution and COVID-19 are not equally available to all, leading to large measurement error bias when using rate-based COVID-19 outcome measures (e.g., incidence rate or mortality rate). Using a quasi-experimental design, we explore whether traffic-related air pollutants cause people with COVID-19 to suffer more extreme health outcomes in New York City (NYC). When we address the previously overlooked challenges to identification, we do not detect causal impacts of increased chronic concentrations of traffic-related air pollutants on COVID-19 death or hospitalization counts in NYC census tracts.

Short-term impact of exposure to ambient air volatile organic compounds on daily clinic visits for urticaria in Kaohsiung, Taiwan.

Air volatile organic compounds (VOCs) cause allergic reaction mainly via the respiratory tract or skin. This study aimed to investigate the association between daily visits by patients with urticaria and short-term changes in exposure to ambient air VOCs. The dependent variable was information from patients with urticaria at a medical center in Kaohsiung, Taiwan, from 2014/01/01 to 2018/07/31. The multivariable model included one-day average 75th percentile values of air VOCs and meteorologic data retrieved from Taiwan Air Quality Monitoring Network database, and was analyzed using a case-crossover study design and conditional logistic regression. Total daily clinic visits for urticaria were significantly positively associated with higher levels of 4 VOCs (ethylbenzene, toluene, m-/p-xylene, and o-xylene (adjusted odds ratio (AOR: 1.03-1.28)) on the visit days, and 10 VOC levels on the fourth lag day (benzene, ethylbenzene, toluene, m-/p-xylene, o-xylene, 1,3,5-trimethylbenzene, n-hexane, methylcyclohexane, cyclohexane, and ethylene (AOR: 1.02-3.02)). Analyses of age and gender subgroups revealed that men showed resistance on the visit day, and women, older, and younger patients were more vulnerable. Men were influenced by higher benzene levels (AOR = 1.24) on the fourth lag day. Higher values of more than 6 VOCs on the fourth lag day significantly affected women, younger and older patients (AOR: 1.04-6.5). The most notable VOCs were methylcyclohexane (women AOR = 3.28, younger AOR = 3.82) and 1,3,5-trimethylbenzene (women AOR = 2.77, older AOR = 6.5) on the fourth lag day, which had the lowest concentrations but highest influence. The concentration of certain air VOCs significantly affected daily visits for urticaria.

Calibrating low-cost sensors using MERRA-2 reconstructed PM2.5 mass concentration as a proxy

Monitoring and mitigating air pollution is the most urgent need of society. In an ideal scenario, air quality must be mapped to the details of the geospatial grid to formulate area-specific roadmaps to mitigate air pollution. However, the heterogeneity of the existing air quality monitoring network in India must be addressed to improve the granularity of the data. Low-cost sensors (LCS) are a potential solution to overcome these challenges. However, their data quality remains an issue. Here, we present an alternative Machine Learning (ML)-based calibration approach for LCS using Modern-Era Retrospective Analysis for Research and Applications, version 2 (or MERRA-2) reconstructed surface-level PM2.5 data as a proxy to the reference mass concentrations at four monitoring sites in NCT-Delhi. LCS were calibrated with the co-located reference and MERRA-2 mass concentrations across sites, and their performance was evaluated. The reference and MERRA-2 reconstructed PM2.5 mass concentrations indicated a better agreement compared to the co-located reference and raw LCS measurements. The R2 values between the MERRA-2 calibrated LCS and reference mass measurements were 0.76 (R2LCS_uncalibrated = 0.68), 0.84 (R2LCS_uncalibrated = 0.76), 0.75 (R2LCS_uncalibrated = 0.65), and 0.78 (R2LCS_uncalibrated = 0.69) at sites S1, S2, S3, and S4, respectively. RMSE values reduced significantly post-calibration across sites. LCS calibrated with the reference monitors produced comparable results to MERRA-2 calibrated LCS across sites, which suggests that the calibration of LCS with MERRA-2 reconstructed PM2.5 is feasible. This new method of LCS calibration will promote citizen-science initiatives and allow regulators to gather high spatiotemporal air quality information.

Air quality sensors and distributional environmental justice: a case study of Salt Lake County, Utah

ABSTRACT Ambient fine particulate matter (PM2.5) causes health problems and is monitored by U.S. federal and state governments. Due to gaps in public monitoring, people rely on non-regulatory air quality monitoring networks (NAQMNs), comprised of low-cost PM2.5 sensors, to obtain air quality information. While environmental justice studies have examined social disparities in PM2.5 and sensor distribution, none have examined disparities in university-purchased vs. privately-purchased sensor distributions. This is important because who purchases sensors (and why) may impact sensor distribution equity. Using multivariable generalized estimating equations, we investigate university-purchased (n=68) and privately-purchased (n=230) sensors in the Salt Lake County, Utah NAQMN with a focus on socioeconomic status (SES), race/ethnicity/immigrant status, and PM2.5 at the census tract-level (n=208). Tracts with higher SES and PM2.5 concentrations had increased sensors per resident. Findings related to SES attenuate when predicting university-purchased vs. privately-purchased sensors. Tracts with greater Hispanic immigrant populations and higher PM2.5 concentrations had increased access to university-purchased sensors. Tracts with higher Asian concentrations were underserved regardless of sensor purchaser. This approach illuminates how university-purchased sensors may enhance distributional environmental justice. Findings have implications for enhancing equitable sensor distribution in NAQMNs.

Applying traffic camera and deep learning-based image analysis to predict PM2.5 concentrations

BackgroundAir pollution has caused a significant burden in terms of mortality and mobility worldwide. However, the current coverage of air quality monitoring networks is still limited. ObjectiveThis study aims to apply a novel approach to convert the existing traffic cameras into sensors measuring particulate matter with a diameter of 2.5 μm or less (PM2.5) so that the coverage of PM2.5 monitoring could be expanded without extra cost. MethodsIn our study, the traffic camera images were collected at a rate of 4 images/h and the corresponding hourly PM2.5 concentration was collected from the reference grade PM2.5 station 3 km away. A customized neural network model was trained to obtain the PM2.5 concentration from images followed by a random forest model to predict the hourly PM2.5 concentration. The saliency maps and the feature importance were utilized to interpret the neural network. ResultsProposed novel approach has a high prediction performance to predict hourly PM2.5 from traffic camera images, with a root mean square error (RMSE) of 0.76 μg/m3 and a coefficient of determination (R2) of 0.98. The saliency map shows neural network focuses on unobstructed far-end road surfaces while the random forest feature importance highlights the first quarter image's significance. The model performance is robust whether weather conditions are controlled or not. ConclusionOur study provided a practical approach to converting the existing traffic cameras into PM2.5 sensors. The deep learning method based on the Resnet architecture in our study can broaden the coverage of PM2.5 monitoring with no additional infrastructure needed.

Assessing the Role of Information and Communication Technology (ICT) in Safeguarding the Environment through the Application of the MOORA Method

Information and Communication Technology (ICT) plays a vital role in bolstering endeavors aimed at safeguarding the environment, and the MOORA method offers a structured approach. ICT involves the use of digital tools and technologies to manage and transmit information, enabling real-time data collection, analysis, and communication. In environmental protection, ICT aids in various ways, such as monitoring air and water quality, tracking wildlife patterns, and managing waste disposal. The MOORA method is a decision-making technique that helps prioritize alternatives based on multiple conflicting criteria. In the context of environmental protection, the MOORA method assists in selecting the most effective ICT solutions. It evaluates various ICT options by considering multiple objectives, such as efficiency, cost-effectiveness, ecological impact, and scalability. MOORA computes ratios to compare alternatives against each criterion, enabling a comprehensive assessment. By assigning weights to the criteria, stakeholders can emphasize specific factors according to their importance. For instance, when choosing between different ICT systems for waste management, the MOORA method can quantify the ecological benefits of reduced emissions, energy savings, and waste reduction against factors like implementation costs and technological feasibility. This systematic evaluation ensures that the chosen ICT solution aligns with the overall goal of environmental protection while considering practical constraints. ICT leverages advanced technologies to bolster environmental protection, and the MOORA method provides a structured approach to assess and prioritize ICT solutions. This combined approach facilitates informed decision-making, leading to the adoption of efficient and sustainable technologies that contribute to a healthier planet. The Smart Grid System (A1), E-waste Recycling Program (A2), Air Quality Monitoring Network (A3), Water Pollution Detection Sensors (A4), Green Supply Chain Management Software (A5), and Virtual Environmental Education Platform (A6) are employed as alternative solutions. These alternatives are assessed based on their ability to achieve Reduction in Environmental Impact (C1), Enhancement of Efficiency (C2), Cost Efficiency (C3), and User-Friendliness (C4).The environmental production of E-waste Recycling Program is got first rank and Smart Grid System is got lowest rank.

An optimized development of urban air quality monitoring network design based on particulate matters.

The design of an air quality monitoring network (AQMN) is the mandatory step to manage air pollution in megacities. Several studies are being done on the location selection of AQMNs based on topography, meteorology, and pollution density. Still, the critical research gap that needs to be addressed is the role of pollutants' importance and prioritization in AQMN. This study aims to utilize the sphere of influence (SOI) method to design an AQMN in a megacity based on particulate matter (PM) as the most serious urban pollutant. Model evaluation was done by employing annual emission inventory data of PM in Tabriz, an industrial and crowded megacity with high exposure to salt particulates, considering 3549 square blocks with a size of 500m * 500m. Then, the SOI methodology utilizing the utility function (UF) approach is applied using MATLAB software calculations to determine optimal air quality monitoring network configurations. A range of numbers of utility functions was yielded for every spot on the map. It resulted in grid city maps with final spots for PM10, PM2.5, and intersecting spots. As a result, ten sites are selected as the best possible locations for the AQMN of a 2 million population city. These results could play a precise and significant role in urban air quality decision-making and management.

Air Quality Characterization and Trend Analysis in a Brazilian Industrialized Metropolitan Area in the Period from 1995 to 2022

The Brazilian population grew approximately 9% from 2006 to 2016, and the number of deaths caused by air pollution increased by 14% in Brazil in the same period. Facing the lack of studies on air quality in the Metropolitan Area of Belo Horizonte (MABH)—the third most populous Brazilian metropolitan area—this study aimed to investigate the air quality and the trends of air pollutant concentrations in the MABH between 1995 and 2022, using data from the air quality monitoring network. The methodology consisted of checking MABHs air quality trends following the WHO air quality guidelines. The Mann–Kendall test was used to check statistically for the possibility of tendencies. The results showed a trend of stability in the concentrations of air pollutants in the MABH without any trend of improvement or worsening. However, the time series of the MABH exposed the challenge of ensuring better air quality that protects human health. Furthermore, the results reinforced the importance of focusing on pollutant sources and exposed the need for improvements in air quality management. Thus, it is essential to reverse the current dismantling scenario of the public environmental agencies in Brazil.

The association of exhaled nitric oxide with air pollutants in young infants of asthmatic mothers

BackgroundExhaled nitric oxide is a marker of airway inflammation. Air pollution induces airway inflammation and oxidative stress. Little is known about the impact of air pollution on exhaled nitric oxide in young infants.MethodsThe Breathing for Life Trial recruited pregnant women with asthma into a randomised controlled trial comparing usual clinical care versus inflammometry-guided asthma management in pregnancy. Four hundred fifty-seven infants from the Breathing for Life Trial birth cohort were assessed at six weeks of age. Exhaled nitric oxide was measured in unsedated, sleeping infants. Its association with local mean 24-h and mean seven-day concentrations of ozone, nitric oxide, nitrogen dioxide, carbon monoxide, sulfur dioxide, ammonia, particulate matter less than 10 μm (PM10) and less than 2.5 μm (PM2.5) in diameter was investigated. The air pollutant data were sourced from local monitoring sites of the New South Wales Air Quality Monitoring Network. The association was assessed using a ‘least absolute shrinkage and selection operator’ (LASSO) approach, multivariable regression and Spearman’s rank correlation.ResultsA seasonal variation was evident with higher median exhaled nitric oxide levels (13.6 ppb) in warmer months and lower median exhaled nitric oxide levels (11.0 ppb) in cooler months, P = 0.008. LASSO identified positive associations for exhaled nitric oxide with 24-h mean ammonia, seven-day mean ammonia, seven-day mean PM10, seven-day mean PM2.5, and seven-day mean ozone; and negative associations for eNO with seven-day mean carbon monoxide, 24-h mean nitric oxide and 24-h mean sulfur dioxide, with an R-square of 0.25 for the penalized coefficients. These coefficients selected by LASSO (and confounders) were entered in multivariable regression. The achieved R-square was 0.27.ConclusionIn this cohort of young infants of asthmatic mothers, exhaled nitric oxide showed seasonal variation and an association with local air pollution concentrations.