Air Quality Assessment Research Articles

Temporal Variation of Indoor (a residential space) and Outdoor PM2.5 in Male’, Maldives

This study aims to assess the Indoor Air Quality (IAQ) inside a one room apartment of about 240 square feet in the Henveiru district in Male’, Maldives. Concentrations of Particulate Matter (PM2.5), one of the most detrimental air pollutants, were measured every 15 minutes from 10-09-2021 to 18-12-2021 using low-cost PM BlueSky sensors inside the building (indoor) and simultaneously outdoor, about 300m away from the indoor sensor. The BlueSky monitors were co-located and harmonized before starting the monitoring. The mean concentrations observed indoor and outdoor for 93 days were 10.1±13.1 µg/m3 and 6.5±4.9 µg/m3, respectively. Both results surpass the annual average of 5 µg/m3 guideline of World Health Organization (WHO). The number of days that surpassed the 24-hour WHO limit was 9 for indoor and 7 for outdoors. The diurnal pattern of PM2.5 shows a drastic accumulation of the pollutant inside the building when the only available ventilation (the window) was closed, which was identified at night and also during the daytime. The recorded PM2.5 levels also aligned with household activities like cooking, cleaning, and dusting. There was a significant moderate correlation (r= 0.48) between outdoor and indoor PM2.5. The I/O ratio was 1.95±3.01 indicating the presence of more indoor sources. This study serves as a preliminary assessment of indoor air quality in Male’, Maldives. Based on its findings, efforts should be directed towards ensuring proper ventilation in residential spaces and continuously monitoring air quality in more location for longer periods to ensure healthy IAQ.

Assessment of indoor air quality in health clubs: insights into (ultra)fine and coarse particles and gaseous pollutants.

Exercising on regular basis provides countless health benefits. To ensure the health, well-being and performance of athletes, optimal indoor air quality, regular maintenance and ventilation in sport facilities are essential. This study assessed the levels of particulate, down to the ultrafine range (PM10, PM2.5, and particle number concentration in size range of 20-1,000 nm, i.e., - PNC20-1000 nm), gaseous pollutants (total volatile organic compounds - TVOCs, CO2, and O3) and comfort parameters (temperature - T, relative humidity - RH) in different functional spaces of health clubs (n = 8), under specific occupancy and ventilation restrictions. In all HCs human occupancy resulted in elevated particles (up to 2-3 times than those previously reported), considering mass concentrations (PM10: 1.9-988.5 μg/m3 PM2.5: 1.6-479.3 μg/m3) and number (PNC 1.23 × 103 - 9.14 × 104 #/cm3). Coarse and fine PM indicated a common origin (rs = 0.888-0.909), while PNC showed low-moderate associations with particle mass (rs = 0.264-0.629). In addition, up to twice-higher PM and PNC were detected in cardiofitness & bodybuilding (C&B) areas as these spaces were the most frequented, reinforcing the impacts of occupational activities. In all HCs, TVOCs (0.01-39.67 mg/m3) highly exceeded the existent protection thresholds (1.6-8.9 times) due to the frequent use of cleaning products and disinfectants (2-28 times higher than in previous works). On contrary to PM and PNC, TVOCs were higher (1.1-4.2 times) in studios than in C&B areas, due to the limited ventilations combined with the smaller room areas/volumes. The occupancy restrictions also led to reduced CO2 (122-6,914 mg/m3) than previously observed, with the lowest values in HCs with natural airing. Finally, the specific recommendations for RH and T in sport facilities were largely unmet thus emphasizing the need of proper ventilation procedures in these spaces.

Ambient air quality assessment at the airports based on a meteorological perspective.

Natural mineral dust episodes elevate particle concentrations and eventually decrease air quality. Air pollutant emissions from aircraft, airport ground operations, and long-range dust transport are producing problems for the aviation sector. Dust transport from the Sahara Desert, one of the primary dust sources globally, significantly affects the eastern Mediterranean basin, including Türkiye. This study investigates the effect of long-range dust transport on particulate matter (PM) concentrations at Turkish airports. Three different approaches were used to analyse dust episodes in the study area. Firstly, routine reports of meteorological conditions at the airports were investigated. For this purpose, airport routine meteorological reports (Metar) and aviation-selected special weather report (Speci) codes, recorded at 11 airports from 2012 to 2021, were used to determine the dust episode days. Secondly, the hourly PM measurement dataset was analyzed from the closest air quality monitoring stations to the airports. Finally, regional atmospheric model results and backward-trajectory analysis were used to validate the dust episodes and determine the dust origin. Results showed that 163 dust episodes occurred during the study period, 17% from North Africa and 12% from the Mediterranean region.

KOMPARASI ALGORITMA DECISION TREE, NAIVE BAYES DAN K-NEAREST NEIGHBOR UNTUK MENENTUKAN KUALITAS UDARA DI PROVINSI DKI JAKARTA

Air quality in DKI Jakarta Province refers to the state and cleanliness of the air in the area at a given time. The type and concentration of air pollutants are among the indicators used to assess air quality. DKI Jakarta's air pollution is severe, causing respiratory irritation, respiratory illnesses, and long-term health issues such as cardiovascular disease and lung cancer. Air pollution can also harm the environment by limiting visibility and harming ecosystems. The problem with the research is that no appropriate and relevant features for predicting air quality were used. The goal of this study is to identify and compare algorithms with the highest accuracy between decision trees. In determining air quality, naive Bayes and k-nearest neighbor are used. According to the findings of the K-5fold evaluation process performed with the RapidMiner tool, the accuracy of the Decision Tree algorithm was 95.89%, the accuracy of the Nave Bayes algorithm was 93.15%, and the accuracy of the K-NN algorithm was 91.78%. Based on these findings, the decision tree method has the greatest or best accuracy when compared to the Nave Bayes and K-NN algorithms.

KAJIAN KUALITAS AIR DI PERAIRAN DESA SUMBERKIMA DAN DESA PEMUTERAN, KECAMATAN GEROKGAK, KABUPATEN BULELENG, PROVINSI BALI

Water quality has a significant impact on aquaculture productivity. Water quality characteristics influence fishing production. This study employs numerous prior research techniques to assess air quality factors such as Total Suspend Solid (TSS), salinity, Sea Surface Temperature (SST), and dissolved oxygen. However, physical elements such as rainfall, which are separated into wet and dry months in this study, have an impact on water quality. The approach was developed using Landsat-8 OLI satellite images. The algorithm's output is validated for data accuracy using Pearson correlation, root mean square error (RMSE), and R-square. The findings suggest that the distribution of water quality in dry and rainy months is low in coastal areas and high in locations adjacent to open waters. Furthermore, it was discovered that the average value of the distribution of TSS in dry months was lower than in wet months, the mean value of the distribution of salinity in dry months was higher than in wet months, the average value of the SST distribution in dry months was higher than in wet months, and the mean value of the dissolved oxygen distribution in dry months was lower than in wet months.
 Keywords: Water suitability; Landsat-8; Water Quality; Remote Sensing

Beyond common urban air quality assessment: Relationship between PM2.5 and black carbon during haze and non-haze periods in Bangkok

The atmospheric pollution burden in Bangkok, Thailand, is likely to be similar to that of other large urban agglomerations. Black carbon (BC) aerosol is one of the key components of PM2.5 (particulate matter with equivalent diameter ≤2.5 μm) pollutants; jointly they adversely affect humans and the environment. This study assessed the BC and PM2.5 ambient concentrations in Bangkok over nine months (November 1, 2020, to July 27, 2021), focusing on seasonal differences during the haze period (Hp; december 2020 to February 2021) and non-haze period (NHP; remaining months). PM2.5 data were measured optically, together with filter sampling of particles at heights of 30, 75, and 110 m above ground level (AGL). A thermal/optical carbon analysis method was used to determine the BC mass concentrations in the collected samples. The results showed that the vertically averaged BC concentration within PM2.5 increased more than 3-fold from 2.82 to 9.09 μg/m3 from the NHP to the HP. The vertical dependence of BC concentration was characterized by an increase in the height AGL in the NHP. In contrast, the opposite trend was observed for the HP. The average values of the PM2.5/BC ratio at all heights AGL showed a seasonal trend, with the BC mass concentration accounting for over 25% of PM2.5 concentration during the peak of the haze season.

A novel algorithm for full-coverage daily aerosol optical depth retrievals using machine learning-based reconstruction technique

The ubiquitous missing values in the satellite-derived aerosol optical depth (AOD) products have always been a challenge for spatial and temporal analysis. To address this concern, we propose a novel data-driven model to attain the full-coverage daily AOD dataset with 0.01° spatial resolution in the Guangdong-Hong Kong-Macao Greater Bay Area (hereafter GBA) from 2010 to 2021. Firstly, the missing values of top-of-atmosphere (TOA) reflectance and surface reflectance of Moderate Resolution Imaging Spectroradiometer (MODIS) caused by cloud contamination, were reconstructed using the Data Interpolating Empirical Orthogonal Functions (DINEOF). Subsequently, a new model was developed for the estimation of AOD which integrates the geographical and temporal encodings into the Light Gradient Boosting Machine (LightGBM) with the inputs of reconstructed TOA/surface reflectance and other influencing variables like meteorological and geographical factors. Results showed that the derived gap-free AOD dataset outperforms the MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD product and agrees well with the ground-based observations, achieving an index of agreement (IOA) of 0.88, R of 0.84, root mean square error (RMSE) of 0.19 and mean absolute error (MAE) of 0.14. Moreover, the derived AOD dataset presents consistent temporal patterns with in-situ measurements, but with more spatial details than other gapless AOD datasets, i.e., Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Overall, this study has developed a promising meteorological framework for the estimation of full-coverage AOD, which can also be applied over other regions. The derived long-term full-coverage daily AOD dataset can also be used for other applications related to climate change, air quality and ecosystem assessment.

Exposure particulate matter (PM2.5) and health risk assessment on informal workers in landfill site, Indonesia

This study investigates the extent of informal workers' exposure to PM2.5 and the associated health risks in the landfill. The location of this investigation is a landfill, which is unique and characteristic of developing countries, especially in Indonesia. This research was conducted in landfills where few studies have been conducted. In location, many are informal workers and cows. Workers in the informal sector are continually exposed to dust for working. To assess air quality, a high volume air sampler and personal dust sampler was used to measure particulate matter concentrations for eight hours. Direct interviews with workers were conducted to determine the health effects of PM2.5 exposure. The PM2.5 concentration ranged from 87.5 to 1080 µg/m3, exceeding the threshold value. The real-time and lifetime Risk Quotients (RQ) were found to be RQ > 1, the risk of causing health problems, precisely 1.61 and 3.02, indicating that the hazard posed a substantial risk to the workers health. The health complaints of dust were cough (74.3 %) and headache (84.6 %). In addition, some respondents also reported symptoms of respiratory infections, such as eye irritation (12.8 %), shortness of breath (12.8 %) and chest tightness (28.2 %). Respiratory infection symptoms such as cough, headache, eye irritation, breathlessness, and wheezing are significantly associated with informal workers exposed to dust. This study concludes that exposure to PM2.5 increases the health risks for landfill Informal workers. A risk management strategy is required to reduce risk to protect workers in the workplace.

NO2 Concentration Estimation at Urban Ground Level by Integrating Sentinel 5P Data and ERA5 Using Machine Learning: The Milan (Italy) Case Study

The measurement of atmospheric NO2 pollution concentrations has become a critical topic due to its impact on human health. Ground sensors are the most popular method for measuring atmospheric pollution, but they can be expensive to purchase, install, and maintain. In contrast, satellite technology offers global coverage but typically provides concentration estimates at the tropospheric level, not at the ground level where most human activities take place. This work presents a model that can be used to estimate NO2 ground-level concentrations in metropolitan areas using Sentinel-5P satellite images and ERA5 meteorological data. The primary goal is to offer a cost-effective solution for Low- and Medium-Income Countries (LMICs) to assess air quality, thereby addressing the air quality measurement constraints. To validate the model’s accuracy, study points were selected in alignment with the Regional Agency for the Environment Protection (ARPA) NO2 sensor network in the Metropolitan City of Milan. The results showed that the RMSE of the model estimations was significantly lower than the standard deviation of the real measurements. This work fills the gaps in the literature by providing an accurate estimation model of NO2 in the Metropolitan City of Milan using both satellite data and ERA5 meteorological data. This work presents as an alternative to ground sensors by enabling more regions to assess their air quality effectively.

Indoor air quality of urban public transportation stations in China: Based on air quality evaluation indexes

Indoor air quality (IAQ) of urban public transportation stations (UPTS) has adverse health impacts on the station employees and commuters. However, there is a lack of comprehensively evaluations of IAQ in waiting rooms of UPTS. Therefore, it is crucial to select appropriate air quality indexes (including fuzzy synthetic index (B), comprehensive index (P), I1 index, and indoor air quality index (IAQI)) to assess air quality and potential health risks. Our study is a subsample of the CPPEHS 2019 study, which included 224 UPTS in 126 cities of China. We found that P index showed an excellent air quality rate of 95.96% in the railway stations and 83.19% in the inter-city bus stations. The P index was correlated with UPTS usage years, useable area, and per passenger useable area. Furthermore, waiting rooms in UPTS with good (OR = 1.9187, 95% CI: 1.1204, 3.2859) and bad (OR = 2.0854, 95% CI: 1.2182, 3.5698) air quality evaluated by P index had a higher risk of rhinitis compared to those with excellent air quality. Similarly, UPTS with good (OR = 2.2202, 95% CI: 1.3427, 3.6711), bad (OR = 1.7897, 95% CI: 1.0807, 2.9637), and serious (OR = 1.7478, 95% CI: 1.0098, 3.0250) air quality evaluated by P index were associated with a higher risk of pharyngitis. These findings suggested that P index is the optimal index for assessing air quality in UPTS, while IAQI may exaggerate indoor air pollution and the B index may underestimate it. Overall, this study aims to identify and evaluate the more suitable air quality index (P) in nationwide UPTS, providing valuable insights for control of IAQ and guiding the air quality management and standards.

Micro-heater embedded Ni-SnO2 ordered nanoporous films: On-chip fabrication for fast, sensitive, and selective gas sensing towards multiple VOCs for air quality monitoring

The ability to simultaneously detect multiple volatile organic compounds (VOCs) is crucial for assessing indoor air quality and addressing significant health hazards. In this study, we present a sacrificial template approach for on-chip fabrication of two-dimensional (2D) Ni-doped SnO2 nanoporous sensing films on micro-heater substrates. The technical analysis shows that the resulting honeycomb-like ultrathin Ni-SnO2 sensing layer on a suspended substrate maintains perfect structural integrity and consistency. The Ni-SnO2 sensors exhibit exceptional characteristics, including a ppb-level detection limit, high sensitivity, fast response/recovery time, and signal repeatability for typical VOCs, while operating at a low power consumption of 25 mW. Additionally, the Ni-doping enhances the sensors' cross-sensitivity to various forms of VOCs, while causing a discernible response inhibition towards flammable or exhaust gases. Furthermore, X-ray photoelectron spectroscopy (XPS) and in-situ Raman spectra reveal a Ni2+-substitution induced surface adsorbed oxygen species enrichment mechanism for sensing enhancement. Thus, the developed Ni-SnO2 sensors hold promise as future candidates for reliable air quality assessment by real-time monitoring of overall concentrations of multiple VOCs.

Assessment of nighttime air quality over an urban location in Indo-Gangetic plain using remote sensing observations

Nocturnal inversion can trap atmospheric pollutants in the lower atmosphere and can significantly impact human health and reduce visibility, particularly in mega-cities. Therefore, nighttime monitoring of atmospheric aerosols and gases is very important. In this paper, we have selected an urban location of Kanpur located in a highly polluted region of Indo-Gangetic Plains (IGP) to study the variability in nighttime aerosols in response to trace gases and meteorological parameters. For this purpose, we have analyzed all the available data of aerosol optical depth at 500 nm (AOD), Angstrom Exponent (440–870) (AE) from AERONET's newly developed direct moon algorithm. We also examined nighttime data of temperature, relative humidity, methane, ozone, and carbon monoxide from AIRS (Atmospheric Infrared Sounder). The average values of nighttime AOD, AE, and precipitable water (PW) were observed to be 0.65 ± 0.04, 1.06 ± 0.21, and 2.86 ± 1.71, respectively. The highest value (0.90 ± 0.05) of lunar AOD is found in November with a corresponding AE value of 1.28 representing the dominance of fine-mode aerosols in Kanpur. In summer, AOD exhibited a slight decreasing trend (slope = −0.008) while AE exhibited an increasing trend (slope = 0.029) indicating that the aerosol burden is decreasing but the contribution of fine mode aerosols is increasing. Urban aerosols were the most abundant type (62.75 %) in the atmosphere of Kanpur during nighttime. Average values of methane, carbon monoxide, and ozone were found to be 1876.30 ± 21.79 ppbv, 127.40 ± 15.12 ppbv, and 40.76 ± 4.55 ppbv, respectively. HYSPLIT cluster analysis on high AOD day indicate the transportation of desert dust and biomass burning aerosols from western and north western regions of India. WTC between AOD and temperature posits a positive correlation in the medium-term period. AOD and carbon monoxide showed an anti-phase association.

ASSESSING INFLUENCE EXERTED BY AMBIENT AIR POLLUTION ON PUBLIC HEALTH IN THE RUSSIAN FEDERATION

Background. One of the factors influencing the health of the population, which is the object of one of the federal projects, is atmospheric air. Many studies have established that atmospheric air pollution with chemical impurities can cause the onset of a new or exacerbation of an existing pathology in humans.
 Purpose. Assess the impact of atmospheric air pollution on the health indicators of the population of the Russian Federation.
 Materials and methods. The hygienic assessment of atmospheric air quality was carried out using the data from Rospotrebnadzor form 18 for 2012-2021, data from the federal information fund for social and hygienic monitoring. The analysis of the primary morbidity of the population was carried out using statistical collections "The incidence of the entire population of Russia with a diagnosis established for the first time in life" for 2012-2021. The calculation of additional cases of population diseases associated with atmospheric air quality was performed in accordance with Methodological recommendations 5.1.0095-14.
 Results. For the period 2012-2021 on the territory of the Russian Federation, the share of atmospheric air samples that do not meet hygienic requirements decreased by 1.69 times. Priority substances for which excesses of standards are registered are: benz(a)pyrene, xylene, hydrogen chloride, nickel oxide, ozone, ethenylbenzene, hydrochloride, methanethiol, particulate matter, formaldehyde, etc. Improvement in atmospheric air quality has reduced the number of additional cases of disease from all causes associated with air pollution. The structure of additional morbidity associated with atmospheric air quality is dominated by diseases of the respiratory, digestive, circulatory, and nervous systems. Additional cases of diseases of the population associated with air quality are registered, depending on the class of diseases, in the territories of 22-42 subjects of the Russian Federation.
 Conclusion. The results obtained can be used in the formation of monitoring programs aimed at reducing priority chemical impurities in the atmospheric air and developing programs for the primary prevention of the population from diseases associated with atmospheric air quality.

EvalCADair: tool for assessing air quality improvement after the deployment of a district heating network

Switching from individual fossil fuel boilers to a district heating network not only allows the reduction of CO2 emissions but may also lead to significant reduction of other air pollutants such as SO2, NOx and PMx, which have well known negative effects on the human health. EvalCADair makes it possible to estimate the annual evolution of the emissions of atmospheric pollutants during the construction phase of a district heating network. This generic tool is applied to the case of the district heating network CADéco, whose heat production consists of centralized heat pumps and gas boilers. Located in Geneva, the case study includes 154 buildings with a total heated surface of 547’000 m2 and an annual heat demand of about 55 GWh. Approximately half of the total surface aera is dedicated to collective residential housing and the rest to various activities. By the end of 2022, 40% of these buildings were connected to the district heating network. Using the tool to estimate the emissions from 2011 to 2022 shows only a small reduction in CO2 emissions, mainly due to the replacement of individual oil boilers with centralized gas boilers. The gain will be more significant when the heat pumps will be activated. The future impact of the activation of the heat pumps is estimated using EvalCADair as a prospective tool.

Low-cost carbon dioxide concentration sensors for assessing air quality in the built environment: an on-site evaluation of their measurement performance

Carbon dioxide (CO2) concentration is commonly used to determine indoor air quality. However, wearable environmental monitoring devices have not considered CO2 content, because they have mainly focused on the outdoor environment. Our goal is to develop a new wearable monitoring system for indoor research purposes that also incorporates a CO2 concentration sensor. We evaluated the performance of two low-cost CO2 sensors, the Senseair K30 and the Sensirion SCD30, by comparing them with a professional reference sensor in two different environments: a Test Cell and the LabZEB of ITC-CNR. A first result shows that both low-cost sensors have acceptable performance, with the K30 showing a trend closer to that of the professional reference sensor and better overall performance. For this reason, we conducted a third test in which we encapsulated the selected K30 low-cost sensor in a 3D-printed, wearable housing that supports the sensor and control unit to see if the 3D-printed part could affect measurement performance. The result shows no interference in measurement performance. This suggests that low-cost sensors can be used in a wearable system to assess indoor air quality for environmental and human-centered research in the built environment.

A systematic comparison of different machine learning models for the spatial estimation of air pollution

Air pollutants harm human health and the environment. Nowadays, deploying an air pollution monitoring network in many urban areas could provide real-time air quality assessment. However, these networks are usually sparsely distributed and the sensor calibration problems that may appear over time lead to missing and wrong measurements. There is an increasing interest in developing air quality modelling methods to minimize measurement errors, predict spatial and temporal air quality, and support more spatially-resolved health effect analysis. This research aims to evaluate the ability of three feed-forward neural network architectures for the spatial prediction of air pollutant concentrations using the measures of an air quality monitoring network. In addition to these architectures, Support Vector Machines and geostatistical methods (Inverse Distance Weighting and Ordinary Kriging) were also implemented to compare the performance of neural network models. The evaluation of the methods was performed using the historical values of seven air pollutants (Nitrogen monoxide, Nitrogen dioxide, Sulphur dioxide, Carbon monoxide, Ozone, and particulate matters with size less than or equal to 2.5 μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu $$\\end{document}m and to 10 μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu $$\\end{document}m) from an urban air quality monitoring network located at the metropolitan area of Madrid (Spain). To assess and compare the predictive ability of the models, three estimation accuracy indicators were calculated: the Root Mean Squared Error, the Mean Absolute Error, and the coefficient of determination. FFNN-based models are superior to geostatistical methods and slightly better than Support Vector Machines for fitting the spatial correlation of air pollutant measurements.Graphical abstract

Impact of Particulate Matter as an Air Quality Pollutant Released from Quarry Activities in Akamkpa, Cross River State, Nigeria

This study examines the impact of particulate matter (PM2.5) and (PM10) as an air quality pollutant released from quarry operations at a quarry site in Akamkpa, Cross River State, Nigeria. Rock quarrying is the practice of obtaining quarry materials, typically rocks, from above or below land surfaces and crushing or grinding them into fragments to produce various rock sizes. For air quality assessment, concentrations of the air pollutants were measured at different locations within and around the quarry site, from a height of 1.5m above ground level. Highly sensitive digital portable meter was used for the measurement of PM2.5 and PM10. The Inverse Distance Weighted model (IDW) tool in ArcGIS was used to perform a spatial interpolation analysis on the data measured from the field monitoring during the dry and wet seasons. The findings indicate that during the dry season, PM2.5 and PM10 concentrations ranged from 292µg/m3 to 127.33µg/m3 and from 377.67µg/m3 to 222.67µg/m3 respectively, while during the wet season, they ranged from 239.67µg/m3 to 92.00µg/m3 and from 276.33µg/m3 to 124µg/m3. The findings suggest that residents of the study area should limit their exposure, especially during the dry season.

Advantages of Slow Sensing for Ambient Monitoring: A Practical Perspective

Air pollution is a ubiquitous threat, affecting 99% of the global populace and causing millions of premature deaths annually. Monitoring ambient air quality is essential, aiding policymakers and environmental agencies in timely interventions. This study delves into the advantages of slower gas sensors over their ultrafast counterparts, with a keen focus on their practicality in real-world scenarios. Slow sensors offer accurate time-averaged exposure assessments, harmonizing with established regulatory benchmarks. Their heightened precision and reliability, complemented by their cost-effectiveness, render them eminently suitable for large-scale deployment. The slow sensing ensures compatibility with regulations, fostering robust risk management practices. In contrast, ultrafast sensors, while claiming rapid detection, despite touting swift detection capabilities, grapple with formidable challenges. The sensitivity of ultrafast sensors to uncontrolled atmospheric effects, fluctuations in pressure, rapid response times, and uniform gas dispersion poses significant hurdles to their reliability. Addressing these issues assumes paramount significance in upholding the integrity of air quality assessments.

Assessment of Atmospheric Air Quality in the Region of Central Kazakhstan and Astana

One of the main issues of environmental protection is the quality of atmospheric air. These problems are especially acute in industrialized regions, where the level of anthropogenic impact is increasing; in Kazakhstan, Central Kazakhstan belongs to such regions. The purpose of this study is to study the relationship between diseases of the population and air pollutants from industrial sources. The research methodology was the use of ArcGIS tools and the construction of a correlation between two parameters: pollution and morbidity in the region. Analysis of mortality rates of the population by main classes of causes of death for 2017–2020 in the regional context in the Republic of Kazakhstan revealed that the mortality rate in 2020 increased by 20.2%. When analyzing the causes of death of the population, diseases associated with the negative impact of the environment were selected. It was noted that, in general, in the Republic of Kazakhstan from 2017 to 2020, there was a downward trend, but in the Karaganda region, in 2020, it increased by 8.7%. In Astana, this indicator also tended to decrease, but as a result, a very strong correlation was found between the incidence of malignant neoplasms in Astana and nitrogen dioxide pollution (Pearson index 0.95).

School exposure to air pollution in the UK and Ireland: Indoor-Outdoor air quality assessment

Abstract Background Globally, 93% of children under the age of 15 (1.8 billion) are exposed to fine particulate matter (PM2.5) levels that might cause respiratory/cardiovascular disorders, impaired neuro/cognitive development, and cancer. Air pollution causes 1/10 deaths in children under the age of 5. Methods The purpose of this observational study was to collect data on indoor and outdoor air quality in 24 schools over 10 school days and compare it with city-wide average air quality levels reported in rural/urban areas in the UK and Northern Ireland. Nitrogen dioxide, volatile organic compounds (VOCs) and PM10,2.5,1 were monitored inside the classrooms and school surroundings with personal pollution monitors. City-wide averages were taken from local reference air quality and ground-source monitoring stations. Data was expressed using the Plume Labs Air Quality Index (PAQI) thresholds [low (0-20), moderate (21-50), high (51-100), very high (101+) pollution level] aligned with WHO's exposure recommendations. Results Despite both AQIs being in the moderate threshold, the school's average indoor AQI was ∼44% higher than the city-wide average. Only 27% of schools’ indoor air quality indexes matched the city-wide average, 59% were higher and 14% were lower. Rural schools had a marginally higher average indoor AQI than urban schools. The primary classroom pollutants observed were VOCs, followed by PM10. The average walk-exposure AQI of 33% of total schools was higher than the city's average. The school reporting the highest average walk AQI was seven times higher than the lowest. Conclusions Poor indoor air quality and higher levels of pollutants were found inside the majority of the schools across the regions. There is a need for mitigation strategies to identify responsible factors for indoor air pollution. A regulatory framework for school ventilation and behavior interventions like nasal washes may reduce pollution exposure and its impact on children's health outcomes and mortality. Key messages • Poor indoor air quality (∼44% higher than the city-wide average) and higher levels of pollutants (volatile organic compounds and PM10) were found inside the majority of the schools across the regions. • A regulatory framework for school ventilation and behavior interventions like nasal washes may reduce air pollution exposure and its impact on children’s health outcomes and mortality.