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With increasing concerns about the impact of outdoor air quality on public health, demand rises for cost-effective, scalable air monitoring solutions. Low-cost sensors offer promise for monitoring outdoor air quality, with potential for widespread deployment. This article presents a comprehensive evaluation methodology for these sensors, focusing on real-world outdoor performance and end-user perspectives. Relevant methodologies for evaluating wireless air monitor with low-cost sensors were sourced from databases. The study outlines rigorous field-testing methodology, addressing sensor accuracy and stability tested in diverse environmental conditions under various climatic and geographical scenarios. This study explores end-user perspectives ensuring data relevance. This research contributes to discourse on low-cost sensor use, emphasizing a comprehensive evaluation framework encompassing technical performance, usability testing, and user perspectives. Insights gained can guide reliable, user-centric air monitoring solutions, enhancing our ability to mitigate health risks from outdoor air pollution. [ABSTRACT FROM AUTHOR]

A new long-term emission inventory called the Inversed Emission Inventory for Chinese Air Quality (CAQIEI) was developed in this study by assimilating surface observations from the China National Environmental Monitoring Centre (CNEMC) using an ensemble Kalman filter (EnKF) and the Nested Air Quality Prediction Modeling System. This inventory contains the constrained monthly emissions of NO x , SO 2 , CO, primary PM 2.5 , primary PM 10 , and non-methane volatile organic compounds (NMVOCs) in China from 2013 to 2020, with a horizontal resolution of 15 km × 15 km. This paper documents detailed descriptions of the assimilation system and the evaluation results for the emission inventory. The results suggest that CAQIEI can effectively reduce the biases in the a priori emission inventory, with the normalized mean biases ranging from -9.1 % to 9.5 % in the a posteriori simulation, which are significantly reduced from the biases in the a priori simulations (-45.6 % to 93.8 %). The calculated root-mean-square errors (RMSEs) (0.3 mg m -3 for CO and 9.4–21.1 µgm3 for other species, on the monthly scale) and correlation coefficients (0.76–0.94) were also improved from the a priori simulations, demonstrating good performance of the data assimilation system. Based on CAQIEI, we estimated China's total emissions (including both natural and anthropogenic emissions) of the six species in 2015 to be as follows: 25.2 Tg of NO x , 17.8 Tg of SO 2 , 465.4 Tg of CO, 15.0 Tg of PM 2.5 , 40.1 Tg of PM 10 , and 46.0 Tg of NMVOCs. From 2015 to 2020, the total emissions decreased by 54.1 % for SO 2 , 44.4 % for PM 2.5 , 33.6 % for PM 10 , 35.7 % for CO, and 15.1 % for NO x but increased by 21.0 % for NMVOCs. It is also estimated that the emission reductions were larger during 2018–2020 (from -26.6 % to -4.5 %) than during 2015–2017 (from -23.8 % to 27.6 %) for most of the species. In particular, the total Chinese NO x and NMVOC emissions were shown to increase during 2015–2017, especially over the Fenwei Plain area (FW), where the emissions of particulate matter (PM) also increased. The situation changed during 2018–2020, when the upward trends were contained and reversed to downward trends for the total emissions of both NO x and NMVOCs and the PM emissions over FW. This suggests that the emission control policies may be improved in the 2018–2020 action plan. We also compared CAQIEI with other air pollutant emission inventories in China, which verified our inversion results in terms of the total emissions of NO x , SO 2 , and NMVOCs and more importantly identified the potential uncertainties in current emission inventories. Firstly, CAQIEI suggested higher CO emissions in China, with CO emissions estimated by CAQIEI (426.8 Tg) being more than twice the amounts in previous inventories (120.7–237.7 Tg). Significantly higher emissions were also suggested over western and northeastern China for the other air pollutants. Secondly, CAQIEI suggested higher NMVOC emissions than previous emission inventories by about 30.4 %–81.4 % over the North China Plain (NCP) but suggested lower NMVOC emissions by about 27.6 %–0.0 % over southeastern China (SE). Thirdly, CAQIEI suggested lower emission reduction rates during 2015–2018 than previous emission inventories for most species, except for CO. In particular, China's NMVOC emissions were shown to have increased by 26.6 % from 2015 to 2018, especially over NCP (by 38.0 %), northeastern China (by 38.3 %), and central China (60.0 %). These results provide us with new insights into the complex variations in air pollutant emissions in China during two recent clean-air actions, which has the potential to improve our understanding of air pollutant emissions in China and their impacts on air quality. All of the datasets are available at 10.57760/sciencedb.13151 (Kong et al., 2023a). [ABSTRACT FROM AUTHOR]

This paper proposes a generalized partially functional linear model with interaction terms. It is suitable for cases where the response variable is scalar, and the predictor variables include a mix of functional and scalar types, while considering the correlations among functional predictor variables. The model uses principal component analysis for dimensionality reduction, employs maximum likelihood estimation to obtain parameter values, proves the asymptotic properties of the estimates, and validates the model's accuracy through data simulation experiments. Finally, the proposed model was applied to investigate the influence of air quality, climate factors, and medical and social indicators, along with their interactions, on cancer incidence, which is a binary response. [ABSTRACT FROM AUTHOR]

This study aims to investigate the seasonal variation and source identification of fluorescent aerosol particles at the monitoring site of the University of Salento in Lecce, southeastern Italy. Utilizing a wideband integrated bioaerosol sensor (WIBS), this research work analyzes data from two specific monitoring days: one in winter (10 January 2024), marked by significant transport of anthropogenic particles from Eastern Europe, and another in early spring (6 March 2024), characterized by marine aerosol sources and occasional desert dust. This study focuses on the seven WIBS particle categories (A, B, C, AB, AC, BC, ABC), which exhibited distinct characteristics between the two days, indicating different aerosol compositions. Winter measurements revealed a predominance of fine-mode particles, particularly soot and bacteria. In contrast, spring measurements showed larger particles, including fungal spores, pollen fragments, and mineral dust. Fluorescence intensity data further emphasized an increase in biological and organic airborne material in early spring. These results highlight the dynamic nature of fluorescent aerosol sources in the Mediterranean region and the necessity of continuous monitoring for air quality assessments. By integrating WIBS measurements with air mass back-trajectories, this study effectively identifies fluorescent aerosol sources and their seasonal impacts, offering valuable insights into the environmental and health implications of aerosol variability in the investigated Mediterranean area. [ABSTRACT FROM AUTHOR]

The well-known Gaussian plume model has proven to be very useful in simulating the atmospheric dispersion of air pollutants (both gaseous and particulates). Nevertheless, the nature of the model presents problems in the actual computation of concentrations when the plume is confined between two parallel boundaries due to the occurrence of multiple reflections. The ground and temperature inversion lid (especially, when the inversion layer is at low levels in the atmosphere) with a chimney stack releasing the effluent below the latter, is one contextual example of horizontal parallel reflecting boundaries. A second example is buildings confining a roadway on either side, with motor vehicles emitting pollution within the street canyon (or urban notch). In such cases, multiple reflections should be accounted for, otherwise the model underpredicts the resulting concentration. This paper presents a mathematical rewriting of the Gaussian plume model equation corrected for multiple reflections when the pollution source is confined between parallel boundaries. The obtained result is most appropriate when the parallel boundaries are rigid, and near-complete reflection is achieved, e.g., street canyon environment (second quoted example). It is worth noting that the relevant mathematical derivations and definitions are all included in the paper to facilitate reading and to ensure comprehensiveness in the presentation. Additionally, the outcome of some preliminary numerical testing is presented. The latter indicates that the new formulation is mathematically stable and yields interesting results. Further numerical investigation and experimental evaluation are merited. [ABSTRACT FROM AUTHOR]

Preliminary monitoring campaigns in three (nonindustrial) indoor environments (a corridor, a coffee room with a kitchenette, and a single-occupancy office, all located in the same public building) were carried out, in which the vertical concentration profiles of airborne particulate matter (inhalable, thoracic, and alveolic fractions, as well as PM10 and PM2.5) and carbon dioxide were determined using two distinct, purpose-built sequential sampling systems. One of the systems was specifically built for use with gas monitors and is based on the organ-pipe sequential air sampling technique. The second system better suited the sequential air sampling of particulate matter. Both systems were electronically controlled. Six receptor levels at heights of 0.25, 0.95, 1.25, 1.65, 2.15, and 2.75 m above the ground/floor were considered. The outcomes of the campaigns are presented. The larger-size particle fractions exhibited the most vertical variation in concentration. With respect to measurements at a height of 1.25 m above the floor, percentage differences as high as 80% were recorded. Given the appreciable measured variation in concentration over a height of approximately three meters, further investigation is warranted, especially in view of the exposure of humans of different heights, e.g., adults and children, and possibly different circumstances, e.g., standing and sitting. [ABSTRACT FROM AUTHOR]

Prescribed burning is a cost-effective method for reducing hazardous fuels in pine- and oak-dominated forests, but smoke emissions contribute to atmospheric pollutant loads, and the potential exists for exceeding federal air quality standards designed to protect human health. Fire behavior during prescribed burns influences above-canopy sensible heat flux and turbulent kinetic energy (TKE) in buoyant plumes, affecting the lofting and dispersion of smoke. A more comprehensive understanding of how enhanced energy fluxes and turbulence are related during the passage of flame fronts could improve efforts to mitigate the impacts of smoke emissions. Pre- and post-fire fuel loading measurements taken during 48 operational prescribed burns were used to estimate the combustion completeness factors (CC) and emissions of fine particulates (PM2.5), carbon dioxide (CO2), and carbon monoxide (CO) in pine- and oak-dominated stands in the Pinelands National Reserve of southern New Jersey. During 11 of the prescribed burns, sensible heat flux and turbulence statistics were measured by tower networks above the forest canopy. Fire behavior when fire fronts passed the towers ranged from low-intensity backing fires to high-intensity head fires with some crown torching. Consumption of forest-floor and understory vegetation was a near-linear function of pre-burn loading, and combustion of fine litter on the forest floor was the predominant source of emissions, even during head fires with some crowning activity. Tower measurements indicated that above-canopy sensible heat flux and TKE calculated at 1 min intervals during the passage of fire fronts were strongly influenced by fire behavior. Low-intensity backing fires, regardless of forest type, had weaker enhancement of above-canopy air temperature, vertical and horizontal wind velocities, sensible heat fluxes, and TKE compared to higher-intensity head and flanking fires. Sensible heat flux and TKE in buoyant plumes were unrelated during low-intensity burns but more tightly coupled during higher-intensity burns. The weak coupling during low-intensity backing fires resulted in reduced rates of smoke transport and dispersion, and likely in more prolonged periods of elevated surface concentrations. This research facilitates more accurate estimates of PM2.5, CO, and CO2 emissions from prescribed burns in the Pinelands, and it provides a better understanding of the relationships among fire behavior, sensible heat fluxes and turbulence, and smoke dispersion in pine- and oak-dominated forests. [ABSTRACT FROM AUTHOR]

Definition: The aim of the entry was to write a substantial contribution that analyses and compares the biological pollution of indoor air, the possibilities of its assessment and the control methods. In addition, the aim of our entry was to review journals covering both commercial and residential buildings. By analysing the above topics from the existing articles, one can have the impression that air pollution is one of the most important problems that need to be solved in the modern world. Adequate air quality is important for maintaining human health, affects the health of ecosystems, including animals, and determines crop production. With the development of civilisation, the quality of air in the atmosphere and indoors is constantly deteriorating. Indoor air pollution can be divided into physical (e.g., noise, inadequate lighting, ionising radiation), chemical (e.g., tobacco smoke, household products) and microbiological (bacteria, viruses, fungi and products of their metabolism) factors. Each of these factors can have a negative impact on a person's health or cause premature death. The entry deals with indoor air pollution, focussing on biological pollutants. It compares different methods available and describes the method of sampling to analyse indoor air pollution and ways to reduce it. [ABSTRACT FROM AUTHOR]

Objectives: Prior to the advent of low-carbon travel, the air pollution arising from vehicle exhaust emissions within the urban canopy layer has remained a significant challenge in the governance of urban atmospheric environments. Methods: This paper presents a series of three-dimensional physical models representing typical urban streets, with varying street interface density (SID). The models employ the computational fluid dynamics (CFD) simulations to assess the impact of SID on airflow patterns and pollutant dispersion. Metrics such as air exchange rate, purging flow rate, average residence time, and pollutant concentration in pedestrian areas are utilized to evaluate the natural ventilation performance and pollutant diffusion capacity across urban streets with different SIDs. Results: The findings illustrate that the CFD simulation approach is an effective means of elucidating the intricate details of airflow and pollutant dispersion within urban street spaces. A reduction in SID on both sides of urban streets has been demonstrated to enhance natural ventilation and air quality. Notably, a decrease in the upstream SID has a more pronounced effect on improving air quality compared to that of the downstream. The arrangement of buildings adjacent to urban streets significantly impacts the overall ventilation performance and pollutant dispersion capacity under different SIDs, with a critical upstream SID value of approximately 0.8. Conclusions: The proposed CFD simulation method can facilitate a comprehensive understanding of airflow and pollutant dispersal mechanisms, thereby enabling more scientific planning and design control of urban street spaces. Further-more, it helps to provide proactive strategies for residents residing near urban streets to cope with traffic-related air pollution risks. [ABSTRACT FROM AUTHOR]

High particulate matter levels in cage-free (CF) houses have led to concerns from producers, as that can pose significant risks to the health and well-being of hens and their caretakers. This study aimed to assess the effectiveness of an electrostatic particle ionization (EPI) + bedding management (BM) treatment in reducing particulate matter (PM) concentrations. Four identical CF rooms each housed 175 hens for six weeks, with two rooms assigned to the EPI + BM treatment (EPI + 20% wood chip topping over 81-week-old litter) and the other two as controls. Measurements of PM were conducted twice a week for 10 min using TSI DustTrak. Additionally, small and large particle concentrations were monitored continuously using a Dylos monitor, with a sampling period of one minute. Footpad scoring was recorded for logistic analysis. Statistical analysis was performed using ANOVA with the Tukey HSD method (p < 0.05). Results demonstrated that the EPI + BM treatment significantly reduced particle counts (37.83% decrease in small particles, 55.90% decrease in large particles) compared to the control group (p < 0.01). PM concentrations were also substantially lowered across different size fractions, ranging from 58.41% to 64.17%. These findings underscore the effectiveness of the EPI + BM treatment in reducing PM in CF houses. The integration of EPI and bedding management innovated in this study holds promise for improving air quality and contributing to the well-being of hens and caretakers in CF housing systems. [ABSTRACT FROM AUTHOR]

The photochemical mechanisms of ozone (O3) formation are complex, and simply reducing nitrogen oxide (NOx) emissions is insufficient to reduce O3 concentrations. The lockdown due to the Coronavirus Disease 2019 (COVID-19) pandemic provided a rare opportunity to explore the mechanisms of O3 formation and evaluate the performance of NOx emission control strategies through practical observations. This study integrates data from ground stations with observations from the TROPOMI sensor on the Sentinel-5P satellite to analyze air quality changes during the two one-month lockdown periods in Guangzhou, China, in March and November 2022. Our analysis particularly focuses on the impact of these lockdowns on O3 and NO2 concentrations, along with shifts in the sensitivity of ozone formation. Furthermore, we have assessed concentration changes of four major pollutants: PM2.5, PM10, SO2, and CO. The results show that the average O3 concentration in Guangzhou decreased during the March lockdown, while the average O3 concentration at three stations in the western part of Guangzhou increased during the November lockdown. The western part of Guangzhou is a VOCs (volatile organic compounds)-limited zone, and the NO2 emission reduction from the lockdown reduced the titration effect on O3, which led to the increase in O3 concentration. Overall, the impact of COVID-19 lockdowns on O3 concentrations depended on the local O3 producing sensitive system, and emissions of other major pollutants were reduced substantially, as reported in many other cities around the world. [ABSTRACT FROM AUTHOR]

With urbanization and increased vehicle usage, understanding the exposure to air pollutants inside the vehicles is vital for developing strategies to mitigate associated health risks. In-vehicle air quality influences the comfort of the driver during long commutes and has gained significant interest. This study focuses on studying in-vehicle air quality in the San Francisco Bay Area in California, an urban setting with significant traffic congestion and varied emission sources and road conditions. Each trip is about 80.5 km (50 miles) in length, with commute times of approximately one hour. Two low-cost portable sensors were employed to simultaneously measure in-vehicle pollutants (PM2.5, PM10, and CO2) during morning and evening rush hours from May 2023 to December 2023. Seasonally averaged PM2.5 varied from 5.07 µg/m3 to 6.55 µg/m3 during morning rush hours and from 4.38 µg/m3 to 4.47 µg/m3 during evening rush hours. In addition, the impacts of local PM2.5, vehicle ventilation settings, and speed of the vehicle on in-vehicle PM concentrations were also analyzed. CO2 buildup in vehicles was studied for two scenarios: one with inside recirculation enabled (RC on) and the other with circulation from outside (RC off). With RC off, CO2 concentrations are largely within the 1100 ppm range recommended by many organizations, while the average CO2 concentrations can be three times high under recirculation mode. This research suggests that low-cost sensors can provide valuable insights into the dynamics of air pollution in the in-vehicle microenvironment, which can better help commuters reduce health risks. [ABSTRACT FROM AUTHOR]

With the increasing number of electric vehicles taking to the roads, the impact of tailpipe emissions on air quality will decrease, while resuspended road dust and brake/tire wear will become more significant. This study quantified PM10 emissions from tire wear under a range of real highway conditions with measurements across different seasons and roadway surface types in Phoenix, Arizona. Tire wear was quantified in the sampled PM10 using benzothiazoles (vulcanization accelerators) as tire markers. The measured emission factors had a range of 0.005–0.22 mg km−1 veh−1 and are consistent with an earlier experimental study conducted in Phoenix. However, these results are lower than values typically found in the literature and values calculated from emissions models, such as MOVES (MOtor Vehicle Emission Simulator). We found no significant difference in tire wear PM10 emission factors for different surface types (asphalt vs. diamond grind concrete) but saw a significant decrease in the winter compared to the summer. [ABSTRACT FROM AUTHOR]

Hydrocarbon traps in the air intake system (AIS) are a common method for controlling evaporative emissions from the air intake path. Several different systems are available, but there is no standard method for determining their efficiencies. Therefore, a component test rig for hydrocarbon traps was developed. Some optimizations were necessary to achieve emission characteristics observed in engine measurements. Using this setup, several measurements were performed on four different hydrocarbon traps. The results were in reasonable agreement with those from engine measurements. Two different hydrocarbon (HC) traps were selected for further studies. In these studies, the repeatability and the dependency of the emission mass level were investigated. Furthermore, the hydrocarbon concentration in the air filter box was determined using point source flame ionization detector (FID) sampling and a metal oxide semiconductor (MOS) sensor. The data showed a correlation with the emission mass determined in a sealed housing emission determination (SHED) test. [ABSTRACT FROM AUTHOR]

The complex nonlinear characteristics of atmospheric chemistry necessitate the development of new methods for calculating source–receptor (S–R) relationships for secondary air pollutants. In this study, the monthly characteristics and S–R relationships of anthropogenic total nitrate (i.e., the sum of N from nitric acid, inorganic nitrate, and peroxyacetyl nitrate) in Northeast Asia were simulated and analyzed. The Community Multiscale Air Quality (CMAQ), Fifth-Generation NCAR/Penn State Mesoscale (MM5), and Sparse Matrix Operator Kernel Emissions (SMOKE) models were employed for air quality modeling, meteorological fields, and emissions processing, respectively. The study area encompassed Republic of Korea, Japan, and most of China. Five source/receptor regions were defined to derive the S–R relationships: three in China, one in Republic of Korea, and one in Japan. To produce data for the calculation of the S–R relationship, several experiments were conducted with a 20% reduction in NOx emission sources. As a result of the S–R relationships, China was rarely impacted by the other two countries. The total depositions in other countries were significantly dominated by China (i.e., 43.5% and 40.7% in Republic of Korea and Japan, respectively, and up to 82.3% in December for Republic of Korea). [ABSTRACT FROM AUTHOR]

Severely cold weather reduces the willingness of residents to open windows while cooking. This results in an insufficient replenishment of makeup air and a reduction in the range hood discharge capacity. For an effective trade-off between indoor air temperature maintenance and air quality aggravation in winter, a new makeup air supply method (ceiling makeup air) was proposed and established both experimentally and numerically. The improvements in the kitchen air environment during cooking were studied through experimental tests and CFD simulations, considering different makeup air arrangements. The results reveal that the ceiling makeup air scheme can significantly reduce the concentration of PM2.5 compared with the cracks makeup air scheme (wherein the kitchen window and door are closed). Moreover, it increased the indoor temperature by over 11.9 °C compared with the open window makeup air scheme. The average relative error between the experimental and simulated data was within 6.1%. Among the considered factors, the size of the air inlet had the largest impact. This was followed by the layout, size, and shape of the ceiling inlets. The ceiling makeup air scheme demonstrated the potential for improving residential kitchen air environments in severely cold regions. [ABSTRACT FROM AUTHOR]

The open burning of sugarcane residue is commonly used as a low-cost and fast method during pre-harvest and post-harvest periods. However, this practice releases various pollutants, including dioxins. This study aims to predict polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs or dioxins) emissions using the grey model (GM (1,1)) and to map the annual flux spatial distribution at the provincial level from 2023 to 2028. An annual emission inventory at the provincial level was developed using the activity rate of dry crop residue from national agencies and literature, following the guidelines set by the United Nations Environment Programme (UNEP). Emission distributions from 2016 to 2022 were then mapped. The average PCDD/F emission values show significant variation among the provinces, averaging 309 pg TEQ/year. Spatially, regions with intensive sugarcane production, such as Lampung and East Java consistently show high emissions, often exceeding 400 pg/m2. Emissions calculated using the UNEP emission factor tend to be higher compared to other factors, due to its generic nature and lack of regional specificity. Emission predictions using GM (1,1) indicate that North Sumatra is expected to experience a steady increase in PCDD/Fs emissions, whereas South Sumatra and Lampung are projected are projected to see a slight decline. This forecast assumes no changes in regional intervention strategies. Most regions in Java Island show a gradual increase in emissions, except for East Java, which is predicted to have a slight decline from 416 pg/year in 2023 to 397 pg/year in 2028. Additionally, regions such as Gorontalo and parts of East Java are projected to remain 'hotspots' with consistently high emissions, highlighting the need for targeted interventions. To address emission hotspots, this study emphasizes the need for cleaner agricultural practices, enhanced enforcement of environmental regulations, and the integration of advanced monitoring technologies to mitigate the environmental and health impacts of PCDD/F emissions in Indonesia. Future studies should consider developing monthly emissions profiles to better account for local agricultural practices and seasonal conditions. The emission data generated in this study, which include both spatial and temporal distributions, are valuable for air quality modeling studies and can help assess the impact of current and future emissions on ambient air quality. [ABSTRACT FROM AUTHOR]

To assess the impact of air pollution on human health in multiple urban areas in Greece, hourly concentrations of common air pollutants (CO, NO2, O3, SO2, PM10, and PM2.5) from 11 monitoring stations in six major Greek cities (Athens, Thessaloniki, Patra, Volos, Ioannina, and Kozani), were used to implement the U.S. EPA's Air Quality Index (AQI) during a seven-year period (2016–2022). In Athens, the capital city of Greece, hourly PM10 and PM2.5 concentrations were also studied in relation to the prevailing wind patterns, while major PM10 episodes exceeding the official daily EU limit (50 μg/m3) were analyzed using the Potential Source Contribution Function (PSCF) in terms of the air mass origin. According to the AQI results, PM10 and PM2.5 were by far the most hazardous pollutants associated with moderate and unhealthy conditions in all the studied areas. In addition, in Athens, Thessaloniki, and Patra, where the benzene levels were also studied, a potential inhalation cancer risk (>1.0 × 10−6) was detected. In Athens, Saharan dust intrusions were associated with downgraded air quality, whilst regional transport and the accumulation of local emissions triggered increased PM10 and PM2.5 levels in traffic sites, especially during cold periods. Our study highlights the need for the development of early warning systems and emission abatement strategies for PM pollution in Greece. [ABSTRACT FROM AUTHOR]

The world is becoming increasingly urbanized, with migration rates often exceeding the infra-structural capacity in cities across the developing world. As such, many migrants must reside in informal settlements that lack civil and health protection infrastructure, including air quality monitoring. Here, geospatial inverse distance weighting and archived Central Pollution Control Board (CPCB) air quality data for neighboring stations from 2018 to 2021 were used to estimate air conditions in five informal settlements in Delhi, India, spanning the 2020 pandemic lockdown. The results showed that WHO limits for PM2.5 and NO2 were exceeded regularly, although air quality improved during the pandemic. Air quality was always better during the monsoon season (44.3 ± 3.47 and 26.9 ± 2.35 μg/m3 for PM2.5 and NO2, respectively) and poorest in the post-monsoon season (180 ± 15.5 and 55.2 ± 3.59 μg/m3 for PM2.5 and NO2). Differences in air quality among settlements were explained by the proximity to major roads and places of open burning, with NO2 levels often being greater near roads and PM2.5 levels being elevated near places with open burning. Field monitoring was performed in 2023 at three settlements and local CPCB stations. Air quality at settlements and their closest station were not significantly different (p < 0.01). However, field data showed that on-site factors within settlements, such as cooking, ad hoc burning, or micro-scale industry, impact air quality on local scales, suggesting health risks are greater in informal settlements because of greater unregulated activity. City-scale models can estimate mean air quality concentrations at unmonitored locations, but caution is needed because such models can miss local exposures that may have the greatest impact on local health. [ABSTRACT FROM AUTHOR]

This study presents an improved wet scavenging process for particles in air quality modeling, focusing on the Korean Peninsula. New equations were incorporated into the air quality chemical transport model (CTM) to enhance the simulation of particulate matter (PM) concentrations. The modified air quality CTM module, utilizing size-dependent scavenging formulas, was applied to simulate air quality for April 2018, a month characterized by significant precipitation. Results showed that the modified model produced more accurate predictions of PM10 and PM2.5 concentrations compared to the original air quality CTM model. The maximum monthly average differences were 5.46 µg/m3 for PM10 and 2.87 µg/m3 for PM2.5, with pronounced improvements in high-concentration regions. Time-series analyses for Seoul and Busan demonstrated better agreement between modeled and observed values. Spatial distribution comparisons revealed enhanced accuracy, particularly in metropolitan areas. This study highlights the importance of incorporating region-specific, size-dependent wet scavenging processes in air quality models. The improved model shows promise for more accurate air quality predictions, potentially benefiting environmental management and policy-making in the region. Future research should focus on integrating more empirical data to further refine the wet scavenging process in air quality modeling. [ABSTRACT FROM AUTHOR]

Assessing air quality in urban areas is vital for protecting public health, and low-cost sensor networks help quantify the population's exposure to harmful pollutants effectively. This paper introduces an innovative method to calibrate air-quality sensor networks by combining CFD modeling with dependable AQ measurements. The developed CFD model is used to simulate traffic-related PM10 dispersion in a 1.6 × 2 km2 urban area. Hourly simulations are conducted, and the resulting concentrations are cross-validated against high-quality measurements. By offering detailed 3D information at a micro-scale, the CFD model enables the creation of concentration maps at sensor locations. Through regression analysis, relationships between low-cost sensor (LCS) readings and modeled outcomes are established and used for network calibration. The study demonstrates the methodology's capability to provide aid to low-cost devices during a representative 24 h period. The precision of a CFD model can also guide optimal sensor placement based on prevailing meteorological and emission scenarios and refine existing networks for more accurate urban air quality representation. The usage of cost-effective air quality networks, high-quality monitoring stations, and high-resolution air quality modeling combines the strengths of both top-down and bottom-up approaches for air quality assessment. Therefore, the work demonstrated plays a significant role in providing reliable pollutant monitoring and supporting the assessment of environmental policies, aiming to address health issues related to urban air pollution. [ABSTRACT FROM AUTHOR]

The air quality in educational campuses affects the health and work efficiency of teachers and students. Studies into this matter are of great significance for optimizing the management of campus living environments. Low-cost online sensors to monitor PM2.5 and CO2 levels were used in typical functional areas of a university campus in Beijing, China, including offices, dormitories, leisure spaces, canteens, and laboratories. By comparing the findings with data from nearby national monitoring stations, the seasonal and spatial variations in PM2.5 and CO2 concentrations were analyzed. Findings indicate PM2.5 levels within the campus were notably lower compared to the surrounding urban environment. There was variation in PM2.5 and CO2 concentrations across different functional areas. Typically, indoor PM2.5 levels were lower than outdoor ones, while CO2 concentrations in enclosed indoor spaces with human activities progressively escalated. The main internal emission sources affecting the PM2.5 level on campus included traffic emissions, dust generated by human activities, and emissions from catering. In contrast, in areas with better green coverage or where a lake system participates in the atmospheric circulation, the PM2.5 level and CO2/PM2.5 were lower. This indicates that the cleansing impact of plants and aquatic systems is instrumental in lowering PM2.5 concentrations, offering healthier leisure spaces. Seasonal variations also impact PM2.5 levels. During the non-heating period, less pollution source emissions led to decreased outdoor PM2.5 concentrations. The campus monitoring sites experienced an approximate 5 µg/m3 and 29 µg/m3 reduction in the average PM2.5 levels as compared to the PM2.5 of the surrounding urban environment, respectively, during the non-heating and heating period. During indoor activities or sleep, CO2 levels can build up to as high as 2303 ppm due to breathing. It is advisable to stay indoors on days when pollution levels are high, whereas on days with clean air, it is healthier to be outdoors or to air out indoor areas by opening windows. Our research provides clearer scientific evidence for incorporating behavioral strategies for improving air quality into both daily work and life. Moreover, the findings are quite meaningful for the widespread adoption of low-cost sensor monitoring in various environments, with applications beyond just the campus settings. [ABSTRACT FROM AUTHOR]

To thoroughly investigate the impact of meteorological conditions and emission changes on winter PM2.5 variation in the Yangtze River Delta (YRD) from 2015 to 2019, we leveraged advanced modeling techniques, namely, the Weather Research and Forecasting (WRF) model and the Nested Air Quality Prediction Model System (NAQPMS). The results revealed that a notable trend of high-PM2.5-concentration regions shifted from coastal areas towards to the inland regions. While emission reduction can effectively reduce the concentration of PM2.5, meteorological changes exert a significant impact on PM2.5 concentration. Unfavorable meteorological changes in 2018 and 2019 emerged as crucial factors driving PM2.5 pollution in the region (up 0~50 µg·m−3). Our findings also shed light on the potential sources and transport pathways of PM2.5 pollution in key cities within the YRD, indicating that the coastal channel of Hebei–Shandong–Jiangsu and the inland channel bordering Hebei, Henan, Shandong, and Anhui serve as major contributors. Light and moderate pollution was predominantly influenced by the medium-distance coastal channel (48~70%). Remarkably, short-distance inland (19~54%) and coastal transportation (33~53%) channels emerged as the primary causes of severe PM2.5 pollution in the YRD. To effectively combat this issue, it is imperative to bolster key control and prevention measures in these regions. [ABSTRACT FROM AUTHOR]

This study presents new glyoxal (CHOCHO) products from the Ozone Monitoring Instrument (OMI) by utilizing updated level 1B irradiance/radiance data (Collection 4) and an updated glyoxal retrieval algorithm. The adoption of Collection 4 contributes to the reduction of artificial signals in differential glyoxal slant column densities (dSCDs) and improved fitting root mean square, and the updated retrieval settings result in fewer negative values of glyoxal dSCDs over oceans and less noisy dSCDs in the South Atlantic Anomaly. On‐line calculations of air mass factors consider interactive physical processes between input parameters. To address persistent trends in glyoxal SCDs over the Pacific Ocean that remain despite these updates, a trend correction is implemented. We evaluate the updated OMI glyoxal products using inter‐comparisons with GOME‐2A/2B glyoxal products. OMI glyoxal products exhibit good spatial and temporal agreement with GOME‐2A/2B, with correlation coefficients of 0.75–0.78 globally and 0.84–0.85 over source regions. Small biases are observed in OMI glyoxal vertical column densities, ranging from −0.2 ± 5.7% to 9 ± 3% in low and high glyoxal conditions, respectively, against GOME‐2A/2B. These advancements contribute to the reliability and accuracy of OMI glyoxal products, enhancing their utility for atmospheric studies and enabling a 20‐year‐long data record suitable for climate studies. Plain Language Summary: This research introduces new glyoxal (CHOCHO) products obtained from a satellite instrument called the Ozone Monitoring Instrument (OMI). For new glyoxal products, we use the updated OMI irradiance/radiance data and update the way we analyze the radiance data. As a result, new OMI glyoxal products show the reduction of errors caused by the instrument and consider interactive physical processes between the surface and atmosphere. The new data matches well with similar data from other satellite instruments (GOME‐2A/B), showing that the improvements are effective. This better data set helps scientists study the air quality over a long period of time. Key Points: We improve Ozone Monitoring Instrument (OMI) glyoxal column measurements with the new radiance data (Collection 4) and updated retrieval algorithmThe new glyoxal measurements show the stable spectral fitting through OMI's lifetimeCompared to GOME‐2A/B, OMI glyoxal products show good spatial (temporal) correlation coefficients of 0.75–0.78 (0.84–0.85), respectively [ABSTRACT FROM AUTHOR]

Vegetables are owed to the restriction of seasonal availability and regional abundance; it becomes essential that vegetables are preserved safely during the off-season. These existing demands look for dried products with high nutritional and organoleptic properties similar to fresh products. This study aimed to investigate the effect of ultrasonication and blanching before hot air drying on the quality attributes of bitter gourd (Momordica charantia). Dried samples were rehydrated to find the efficiency of pre-treatment and physicochemical properties. M. charantia slices were pre-treated with ultrasonication and blanched and dried at two different temperatures, 50 °C and 60 °C. M. charantia pre-treated using ultrasonication and dried at 60 °C reduces the drying time to 50% and rehydration time to 40% compared to untreated samples. Physico-chemical analysis of ultrasonicated samples revealed to have better retention of moisture (dried − 3.6%, rehydrated – 88%), Colour ΔE (dried – 9.07, rehydrated – 1.6), ascorbic acid (dried – 513, rehydrated – 310 mg/100 g), phenol (dried – 302, rehydrated – 231 GAE mg/100 g) and β-carotene (dried – 68 µg/100 g, rehydrated – 39 µg/100 g) compared to blanching. [ABSTRACT FROM AUTHOR]

Incubation of an empty nest is a rarely detected phenomenon in birds but has been documented in a diversity of species. The causes of this breeding anomaly are unclear, but previous accounts have occurred in regions with elevated air pollution. We provide a detailed account of a female Black-throated Blue Warbler (Setophaga caerulescens) incubating an empty nest for 8 d at the Hubbard Brook Experimental Forest, New Hampshire, USA, May–June 2023, and explore possible explanations based on a literature review of this breeding anomaly. Our observation coincided with the presence of smoke from widespread wildfires in Quebec, Canada. The fires were caused by record temperatures and dry conditions. Wildfire smoke increased air particle pollution. We found 231 published accounts of 11 species incubating empty nests. Cavity-nesters in Europe comprised 45% of species (5 of 11) and 96% of accounts (225 of 231). Although many accounts lacked a clear explanation, the most common factors hypothesized to explain incubating empty nests were exposure to environmental pollution (81% of accounts, 167 of 206), calcium deficiency from environmental acidification (19% of accounts, 40 of 206), and disease and/or infertility (4% of accounts, 8 of 206). Our synthesis of published observations highlights the need for more data on individuals expressing this unusual behavior. Such data would improve our understanding of how egg laying and reproductive success respond to intrinsic and environmental factors, including wildfire emissions, which are forecasted to increase with climate change. [ABSTRACT FROM AUTHOR]

Afghanistan faces severe air quality issues in major cities due to various sources like transportation, domestic energy use, and industrial activity. This study investigates PM2.5 spatiotemporal variability and its future relationship with six meteorological variables: precipitation, temperature, dewpoint temperature, wind speed, boundary layer height and surface pressure. This study aims to assess the spatiotemporal variability of PM2.5 concentrations in Afghanistan and derive models for predicting PM2.5 from the six variables. Satellite-measured PM2.5 and six reanalyses (ERA5) meteorological datasets for 1998–2020 were used as predictors. Three machine learning models, AdaBoost, Random Forest (RF), and Support Vector Machine (SVM), were used to develop the annual and seasonal PM2.5 concentration prediction model. Results suggest PM2.5 levels ranging from 60–80 µg/m3 in northern, southern, and western regions, while other areas experience lower levels (12–50 µg/m3). The lowest PM2.5 concentrations are in the Hindu Kush mountain range. Summer exhibited the highest PM2.5 concentrations, reaching a maximum of 137.4 µg/m3 and an average of 48.5 µg/m3. Among the prediction models, RF performed best in predicting PM2.5 across Afghanistan, as evidenced by the evaluation metrics: NRMSE (59.2), RSR (0.59), rSD (0.75), and higher values of NSE (0.65), R2 (0.65), and KGE (0.68). The geographical and seasonal distribution of observed PM2.5 distribution was very similar to the PM2.5 estimated using RF compared to the other two models. The analysis showed that air temperature, precipitation, wind speeds, and boundary layer heights play significant roles in PM2.5 distribution. However, the relationship between precipitation and PM2.5 was more pronounced than other meteorological variables. [ABSTRACT FROM AUTHOR]

Graphics processing units (GPUs) are becoming a compelling acceleration strategy for geoscience numerical models due to their powerful computing performance. In this study, AMD's heterogeneous-compute interface for portability (HIP) was implemented to port the GPU acceleration version of the piecewise parabolic method (PPM) solver (GPU-HADVPPM) from NVIDIA GPUs to China's domestic GPU-like accelerators like GPU-HADVPPM4HIP. Further, it introduced the multi-level hybrid parallelism scheme to improve the total computational performance of the HIP version of the CAMx (Comprehensive Air Quality Model with Extensions; CAMx-HIP) model on China's domestic heterogeneous cluster. The experimental results show that the acceleration effect of GPU-HADVPPM on the different GPU accelerators is more apparent when the computing scale is more extensive, and the maximum speedup of GPU-HADVPPM on the domestic GPU-like accelerator is 28.9 × faster. The hybrid parallelism with a message passing interface (MPI) and HIP enables achieving up to a 17.2 × speedup when configuring 32 CPU cores and GPU-like accelerators on the domestic heterogeneous cluster. The OpenMP technology is introduced further to reduce the computation time of the CAMx-HIP model by 1.9 ×. More importantly, by comparing the simulation results of GPU-HADVPPM on NVIDIA GPUs and domestic GPU-like accelerators, it is found that the simulation results of GPU-HADVPPM on domestic GPU-like accelerators have less difference than the NVIDIA GPUs. Furthermore, we also show that the data transfer efficiency between CPU and GPU has a meaningful essential impact on heterogeneous computing and point out that optimizing the data transfer efficiency between CPU and GPU is one of the critical directions to improve the computing efficiency of geoscience numerical models in heterogeneous clusters in the future. [ABSTRACT FROM AUTHOR]

Global warming has led to the continuous deterioration of the living environment, in which air quality directly affects human health. In addition, the severity of the COVID-19 pandemic has further increased the attention to indoor air quality. Indoor clean air quality is not only related to human health but also related to the quality of the manufacturing environment of clean rooms for numerous high-tech processes, such as semiconductors and packaging. This paper proposes a comprehensive model for evaluating, analyzing, and improving the operational performance of air cleaning equipment. Firstly, three operational performance evaluation indexes, such as the number of dust particles, the number of colonies, and microorganisms, were established. Secondly, the 100(1 −   α)% upper confidence limits of these three operational performance evaluation indexes were deduced to construct a fuzzy testing model. Meanwhile, the accumulated value of ϕ was used to derive the evaluation decision-making value. The proposed model can help companies identify the key quality characteristics that need to be improved. Furthermore, the competitiveness of cooperative enterprises towards smart manufacturing can be strengthened, so that enterprises can not only fulfill their social responsibilities while developing the economy but also take into account the sustainable development of enterprises and the environment. [ABSTRACT FROM AUTHOR]

Recent advances in sensor technology for air pollution monitoring open new possibilities in the field of environmental epidemiology. The low spatial resolution of fixed outdoor measurement stations and modelling uncertainties currently limit the understanding of personal exposure. In this context, air quality sensor systems (AQSSs) offer significant potential to enhance personal exposure assessment. A pilot study was conducted to investigate the feasibility of the NO2 sensor model B43F and the particulate matter (PM) sensor model OPC-R1, both from Alphasense (UK), for use in epidemiological studies. Seven patients with chronic obstructive pulmonary disease (COPD) or asthma had built-for-purpose sensor systems placed inside and outside of their homes at fixed locations for one month. Participants documented their indoor activities, presence in the house, window status, and symptom severity and performed a peak expiratory flow test. The potential inhaled doses of PM2.5 and NO2 were calculated using different data sources such as outdoor data from air quality monitoring stations, indoor data from AQSSs, and generic inhalation rates (IR) or activity-specific IR. Moreover, the relation between indoor and outdoor air quality obtained with AQSSs, an indoor source apportionment study, and an evaluation of the suitability of the AQSS data for studying the relationship between air quality and health were investigated. The results highlight the value of the sensor data and the importance of monitoring indoor air quality and activity patterns to avoid exposure misclassification. The use of AQSSs at fixed locations shows promise for larger-scale and/or long-term epidemiological studies. [ABSTRACT FROM AUTHOR]

This study presents a fit-for-purpose lab and field evaluation of commercially available portable sensor systems for PM, NO2, and/or BC. The main aim of the study is to identify portable sensor systems that are capable of reliably quantifying dynamic exposure gradients in urban environments. After an initial literature and market study resulting in 39 sensor systems, 10 sensor systems were ultimately purchased and benchmarked under laboratory and real-word conditions. We evaluated the comparability to reference analyzers, sensor precision, and sensitivity towards environmental confounders (temperature, humidity, and O3). Moreover, we evaluated if the sensor accuracy can be improved by applying a lab or field calibration. Because the targeted application of the sensor systems under evaluation is mobile monitoring, we conducted a mobile field test in an urban environment to evaluate the GPS accuracy and potential impacts from vibrations on the resulting sensor signals. Results of the considered sensor systems indicate that out-of-the-box performance is relatively good for PM (R2 = 0.68–0.9, Uexp = 16–66%, BSU = 0.1–0.7 µg/m3) and BC (R2 = 0.82–0.83), but maturity of the tested NO2 sensors is still low (R2 = 0.38–0.55, Uexp = 111–614%) and additional efforts are needed in terms of signal noise and calibration, as proven by the performance after multilinear calibration (R2 = 0.75–0.83, Uexp = 37–44%)). The horizontal accuracy of the built-in GPS was generally good, achieving <10 m accuracy for all sensor systems. More accurate and dynamic exposure assessments in contemporary urban environments are crucial to study real-world exposure of individuals and the resulting impacts on potential health endpoints. A greater availability of mobile monitoring systems capable of quantifying urban pollutant gradients will further boost this line of research. [ABSTRACT FROM AUTHOR]

Smart cities rely on a network of sensors to gather real-time data on various environmental factors, including air quality. This paper addresses the challenges of improving the accuracy of low-cost particulate matter sensors (LCPMSs) which can be compromised by environmental conditions, such as high humidity, which is common in many urban areas. Such weather conditions often lead to the overestimation of particle counts due to hygroscopic particle growth, resulting in a potential public concern, although most of the detected particles consist of just water. The paper presents an innovative design for an indicative air-quality measuring station that integrates the particulate matter sensor with a preconditioning subsystem designed to mitigate the impact of humidity. The preconditioning subsystem works by heating the incoming air, effectively reducing the relative humidity and preventing the hygroscopic growth of particles before they reach the sensor. To validate the effectiveness of this approach, parallel measurements were conducted using both preconditioned and non-preconditioned sensors over a period of 19 weeks. The data were analyzed to compare the performance of the sensors in terms of accuracy for PM1, PM2.5, and PM10 particles. The results demonstrated a significant improvement in measurement accuracy for the preconditioned sensor, especially in environments with high relative humidity. When the conditions were too severe and both sensors started measuring incorrect values, the preconditioned sensor-measured values were closer to the actual values. Also, the period of measuring incorrect values was shorter with the preconditioned sensor. The results suggest that the implementation of air preconditioning subsystems in LCPMSs deployed in smart cities can provide a cost-effective solution to overcome humidity-related inaccuracies, thereby improving the overall quality of measured air pollution data. [ABSTRACT FROM AUTHOR]

The rapid transformation from conventional vehicles to electric vehicles (EVs) holds promise for enhancing air quality, reducing CO2 emissions, revitalizing the renewable energy market, and enhancing countries' economies. This study investigated the performance of EVs in Jordan and the key challenges hindering their rapid development. It implemented a structured questionnaire of 202 EV owners/drivers and 15 interviews with technicians at many service centers. Findings revealed that Jordan leads among MENA countries in Battery Electric Vehicles(BEVs) market share and growth rate, with 52% market share of BEVs new cars in 2023, projected to surpass 80% by 2025. This rapid development is tied up with many internal and external influences; government backing, high fossil fuel prices, and tax exemptions are common causes that enhanced the market of EVs. Respondents highlighted prevalent issues of BEVs, including the rapid battery depletion, low battery capacity, electric inverter malfunctions, battery overheating, low performance in hot and cold climates, and inflating costs. Finally, this study recommends educational institutions to offer targeted training programs for EV maintenance and service, and the government should provide more incentives for this industry, and combat the influx of counterfeit charging accessories, batteries, and spare parts. [ABSTRACT FROM AUTHOR]

The Planetary Boundary Layer Height (PBLH) significantly impacts weather, climate, and air quality. Understanding the global diurnal variation of the PBLH is particularly challenging due to the necessity of extensive observations and suitable retrieval algorithms that can adapt to diverse thermodynamic and dynamic conditions. This study utilized data from the Cloud-Aerosol Transport System (CATS) to analyze the diurnal variation of PBLH in both continental and marine regions. By leveraging CATS data and a modified version of the Different Thermo-Dynamics Stability (DTDS) algorithm, along with machine learning denoising, the study determined the diurnal variation of the PBLH in continental mid-latitude and marine regions. The CATS DTDS-PBLH closely matches ground-based lidar and radiosonde measurements at the continental sites, with correlation coefficients above 0.6 and well-aligned diurnal variability, although slightly overestimated at nighttime. In contrast, PBLH at the marine site was consistently overestimated due to the viewing geometry of CATS and complex cloud structures. The study emphasizes the importance of integrating meteorological data with lidar signals for accurate and robust PBLH estimations, which are essential for effective boundary layer assessment from satellite observations. [ABSTRACT FROM AUTHOR]

Haze aerosols have a profound impact on air quality and pose serious health risks to the public. Due to its geographical location, Beijing experienced haze events in the spring of 2024. Lidar is an active remote sensing technology with a high spatiotemporal resolution and the ability to classify aerosols, and it is essential for effective haze monitoring. This study utilizes fluorescence–Raman–Mie polarization lidar with an emission wavelength of 355 nm, employing the δ p - G f method based on the particle depolarization ratio at 355 nm ( δ p 355 ) and the fluorescence capacity ( G f ), and combines meteorological data and backward-trajectory analysis to observe and classify low-altitude haze aerosols in Beijing during the spring of 2024. Notably, a mining dust event with strong fluorescence backscatter was detected. The haze aerosols were categorized into three types: pollution aerosols, desert dust, and mining dust. Their optical properties were summarized and compared. Desert dust showed a particle depolarization ratio range of 0.23–0.39 and a fluorescence capacity range from 0.18 × 10−4 to 0.63 × 10−4. Pollution aerosols had a larger fluorescence capacity but a lower depolarization ratio compared to desert dust, with a fluorescence capacity ranging from 0.55 × 10−4 to 1.10 × 10−4 and a depolarization ratio ranging from 0.10 to 0.17. Mining dust shared similar depolarization characteristics with desert dust but had a larger fluorescence capacity, ranging from 0.71 × 10−4 to 1.23 × 10−4, with a depolarization ratio range of 0.30–0.39. This study validates the effectiveness of the δ p 355 - G f method in classifying low-altitude haze aerosols in Beijing. Additionally, it offers a new perspective for more detailed dust classification using lidar. Furthermore, utilizing the δ p 355 - G f classification method and the δ p 355 - G f distributions of three typical aerosol samples, we developed a set of equations for the analysis of mixed aerosols. This method facilitates the separation and fraction analysis of aerosol components under various mixing scenarios. It enables the characterization of variations in the three types of haze aerosols at different altitudes and times, offering valuable insights into the interactions between desert dust, mining dust, and pollution aerosols in Beijing. [ABSTRACT FROM AUTHOR]

Dental practices have evolved over time, introducing standardized safety protocols and enhancing safety standards. Regulatory bodies like OSHA, CDC, and ADA have set standards for infection control, chemical safety, and occupational health. Dental clinics face biological, chemical, and physical hazards. Maintaining high standards of EHS is crucial for patient and professional health, regulatory compliance, patient trust, and environmental sustainability. The COVID-19 pandemic has necessitated extra precautionary measures, and infection control measures have improved. Patients generally accept dental-dams and ergonomic interventions for infection control and safety. The aim of this study is to evaluate and enhance the environmental health and safety (EHS) protocols within dental clinics by examining key parameters that influence the well-being of both patients and dental professionals. Specifically, the study will focus on assessing air quality, infection control compliance, and the presence of isolated microorganisms in the workplace during invasive dental procedures. This study aims to assess environmental health and safety (EHS) parameters in dental clinics using a cross-sectional observational design. Participants include dental professionals and patients, who will evaluate infection control compliance and provide insights. Data will be collected from ten hospitals in Saudi Arabia, focusing on air quality, infection control compliance, and the presence of isolated microorganisms during invasive dental procedures. The study found that dental clinics have high concentrations of PM2.5 and CO2 in treatment rooms, indicating dental procedures. However, higher levels in waiting rooms suggest potential ventilation issues. Adherence to infection control protocols varied among dental professionals, with hand hygiene practices more prevalent among dental assistants. The study also highlighted the need for effective air filtration and control measures. The study on environmental health and safety in dental clinics reveals critical parameters affecting safety and quality. It highlights air quality, infection control compliance, and microbial contamination, emphasizing the need for high standards and effective cleaning and disinfection. [ABSTRACT FROM AUTHOR]

Global and South African populations continue to increase exponentially, with large flows of persons into urban centres. Urban air quality and bioaerosol are therefore becoming an increasingly important consideration in the face of a growing burden of allergic respiratory diseases. More than 20% of South Africans have asthma, with morbidity being disproportionately high compared to high-income countries (HICs). Air pollutants (particularly particulate matter and ozone), pollen and fungal spores are known triggers and exacerbating factors in asthma and, alarmingly, the levels of air pollutants remain high in hotspot areas despite robust air-quality legislation in South Africa. The concern is that genetically and environmentally vulnerable children in low- and middle-income countries (LMICs) such as South Africa will have asthma triggered and exacerbated disproportionately by poor air quality. Urban green areas are increasingly being recognised by many stakeholders as important possible mitigation tools. However, multiple factors must be considered for having an optimal green space design. In this review, we bring together experts across the fields of health, air quality and aerobiology to outline the intersection between asthma and air quality and/or aerobiology in South Africa. We highlight the importance of careful multidisciplinary green space design as a possible healing tonic. We conclude that detailed studies combining air quality, pollen dispersal and health issues are needed to derive more precise exposure--response functions of the green spaces in South Africa and other LMICs. [ABSTRACT FROM AUTHOR]

Background Recent evidence has linked air pollution with frailty, yet little is known about the role of NO2 in this association. Our aim was to assess the association between frailty and NO2 air concentrations in Mexican older adults. Methods We used georeferenced data from the population-based Nutrition and Health Survey in Mexico (NHNS) 2021, representative of national and subnational regions, to measure a frailty index based on 31 health deficits in adults aged 50 and older. Air pollution due to NO2 concentrations was estimated from satellite images validated with data from surface-level stations. Maps were produced using Jensen's Natural break method. The association of frailty and NO2 concentrations was measured using the frailty index (multivariate fractional response logit regression) and a frailty binary variable (frailty index [FI] ≥0.36, multivariate logit regression). Results There was a positive and significant association of the frailty index with the NO2 concentrations, adjusting for age, sex, urban and rural area, years of education, socioeconomic status, living arrangement, particulate matter smaller than 2.5 microns, and indoor pollution. For each standard deviation increase in NO2 concentrations measured 10 years before the survey, the odds of being frail were 15% higher, and the frailty index was 14.5% higher. The fraction of frailty attributable to NO2 exposure ranged from 1.8% to 23.5% according to different scenarios. Conclusions Frailty was positively associated with exposure to NO2 concentrations. Mapping frailty and its associated factors like NO2 air concentrations can contribute to the design of targeted pro-healthy aging policies. [ABSTRACT FROM AUTHOR]

A sustainable city is a smart city with a minimal impact on the environment, by incorporating technologies to reduce pollution. Traffic congestion which is a major concern contributes to global warming and climate change. Traffic forecasting projects future traffic patterns, using historical and current data to enhance traffic flow management. We propose a whole novel approach for predicting traffic congestion rate based on air quality data. We developed a new ensemble voting model based on Long Short Term Memory (LSTM) and Polynomial Regression (PolReg) models that use a new voting thresholded algorithm instead of the existing voting ones. The hyperparameters were optimized with the Genetic Agorithm, to overcome the non-stationarity of time series. A comparative study with the literature confirmed that our framework outperforms existing researches by keeping an absolute effectiveness according to learning curves, with Mean Absolute Error of 0.04, R-Squared of 0.93, and Root Mean Square Error (RMSE) of 0.05. [ABSTRACT FROM AUTHOR]

Comprehensive Air Quality Model with extensions (CAMx)–Decoupled Direct Method (DDM) simulations of first-order ozone (O 3) sensitivity to nitrogen oxides (NO x) and volatile organic compounds (VOCs) emissions were performed and combined with modelled P H 2 O 2 / P HN O 3 ratios to obtain a range of thresholds for determining O 3 -sensitivity regimes for different areas of China. Utilising the new threshold ranges for photochemical indicators, the method for determining O 3 formation in the Ozone Source Apportionment Technology (OSAT) module within CAMx was improved by a dynamically varied threshold of P H 2 O 2 / P HNO 3 ratio. The O 3 concentration contributions in the newly added transition regime were apportioned to NO x and VOCs emissions in proportion to the relationship between the P H 2 O 2 / P HNO 3 ratio and first-order O 3 sensitivity. The source contributions of O 3 concentrations from different emission sectors from June to September 2019 were compared using the original and improved CAMx-OSAT. The results showed that the O 3 concentration contributions changed significantly in the NO x -limited regime, with a maximum decrease of 21.89%, while the contributions increased by up to 7.57% in the VOC-limited regime, and were within 15 µg/m3 in the transition regime. The modified OSAT module enabled a more sophisticated attribution of O 3 to precursor emissions and may have far-reaching implications for informing O 3 pollution control policy. [ABSTRACT FROM AUTHOR]

Air pollution affects not only the air in cities but also extends to all indoor environments (homes, offices, schools, public places, transportation, etc.), where we spend between 80% and 90% of our time. Both indoor and outdoor air quality have emerged as significant health concerns and are integral to national strategies implemented by health and environmental institutes in each country. Recently, complaints regarding outdoor air quality have risen in cities, primarily due to automobile traffic and industrial activities in urban areas, and also indoors within homes, offices, and schools. The following paper presents a methodology for the calibration of low-cost monitoring stations based on measurements in a couple of cities in Colombia as part of the development of a project to reduce the environmental awareness gap in urban areas for the estimation of the air quality through low-cost, flexible, modular, and mobile air quality monitoring station design that could be used to assess air pollution in different indoor and outdoor environments. With the implementation of the low-cost stations, we have calibrated and evaluated the performance of the stations using usual linear regression methods, but we have also explored the use of unsupervised estimation with the help of machine learning algorithms, specifically with Random Forest estimators. We have found a significant improvement with using Random Forest for station calibration compared with those found using simple linear regressions for calibration effects. We have found that all the models offer a significant improvement in terms of RMSE. The regression model improves RMSE by up to 70%, while the multiple regression model does so by up to 73%. However, it is the Random Forest that shows the most remarkable improvement, with a reduction in RMSE of up to 86%. [ABSTRACT FROM AUTHOR]

How atmospheric pollution is perceived by urban dwellers has long been a topic of interest within geography and the social sciences, whether to draw attention to environmental injustices, to better understand the materialities and affects associated with polluted air, or to grasp how people 'tune in' to polluted matter. In this paper, we draw on three interrelated geographical and social science literatures on polluted air to inform our exploration of how residents of an industrial town in the UK encounter and perceive localised ambient air pollution. Using creative methods, we explore residents' narrative accounts of everyday life in the town, revealing how their engagements with the matter of pollution over time are drawn from multiple registers, giving rise to a plurality of perceptions filled with tensions between near and far, and between past, present and future, producing an ambiguous atmosphere all of its own. The paper contributes to geographic explorations of urban atmospheres an understanding of how they are differently experienced and known, and how residential perceptions might persist or change over different timescales. [ABSTRACT FROM AUTHOR]

As Industry 4.0 develops, enterprises are investing in the industrial intelligent transformation. Using unique data from Chinese industrial enterprises, this paper investigates whether air quality affects the upgrading of industrial intelligence in enterprises. We find that decreased air quality improves industrial intelligence for enterprises, as enterprises increase their use of intelligent industrial robots when air pollution causes shortages of labour quantity and efficiency. Further analysis shows that large-scale enterprises and enterprises in areas with high economic development are more inclined to develop industrial intelligence in response to decreased air quality than their counterparts. In addition, the results indicate that the intelligent transformation of enterprises can improve their performance. This study provides a new perspective on the impact of environmental issues on corporate behaviour. [ABSTRACT FROM AUTHOR]

We study statistical inference procedures in coarsened time series through the generalized method of moments. A new model for the coarsened time series via multiple potential outcomes is proposed. It can be naturally extended for inferring multi‐variate coarsened time series. We show that this framework generates a general class of estimators. It neatly generalizes the classical Horvitz–Thompson estimator for handling coarsened time series data. Asymptotic properties, including consistency and limiting distribution, of the proposed estimators are investigated. Estimators of the optimal weight matrix and the long‐run covariance matrix are also derived. In particular, confidence intervals of the mean function of the potential outcome as a function of coarsening index can be constructed. A real‐data application on air quality in the USA is investigated. [ABSTRACT FROM AUTHOR]

Ensemble learning synthesizes the advantages of different models and has been widely applied in the field of spatial prediction. However, the nonlinear constraints of spatial heterogeneity on the model ensemble process make it difficult to adaptively determine the ensemble weights, greatly limiting the predictive ability of the ensemble learning model. This paper therefore proposes a novel geographical spatial heterogeneous ensemble learning method (GSH-EL). Firstly, the geographically weighted regression model, geographically optimal similarity model, and random forest model are used as three base learners to express local spatial heterogeneity, global feature correlation, and nonlinear relationship of geographic elements, respectively. Then, a spatially weighted ensemble neural network module (SWENN) of GSH-EL is proposed to express spatial heterogeneity by exploring the complex nonlinear relationship between the spatial proximity and ensemble weights. Finally, the outputs of the three base learners are combined with the spatial heterogeneous ensemble weights from SWENN to obtain the spatial prediction results. The proposed method is validated on the PM2.5 air quality and landslide dataset in China, both of which obtain more accurate prediction results than the existing ensemble learning strategies. The results confirm the need to accurately express spatial heterogeneity in the model ensemble process. [ABSTRACT FROM AUTHOR]

Establishment of saplings of woody ornamental perennials is a challenging task during mass plantation campaigns in degraded landscapes. Consequently, large proportions of transplanted saplings fail to flourish. A two-year field study was conducted with an objective of raising quality saplings of five woody shrubs (Buxus sempervirens, Lawsonia inermis, Murraya paniculata, Schefflera arboricola and Tecoma stans) under the subtropical environment of Northwest India to address this issue. Six-month-old, rooted seedlings of these shrubs were planted in two container types: air-pots and poly bags (Control), arranged in a randomised complete block design replicated three times. A two-year study revealed that container type significantly altered the root and shoot characteristics. The saplings raised in air-pots showed a higher percentage increment for collar diameter, plant height, first order lateral roots, root and shoot dry weight as compared to those grown in poly bags. Observations revealed a higher order of deflected, circled, or kinked roots in the poly bag raised saplings, along with a higher increment in root length due to unchecked extension of tap root(s). Air-pots also exhibited better indices with acceptable root: shoot ratio and sturdiness quotient with negligible root deformities, a necessary pre-requisite for better survival and establishment of transplanted saplings. [ABSTRACT FROM AUTHOR]

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This study presents an in-depth analysis of air quality in Astana, Kazakhstan, utilizing both mobile and stationary air monitoring systems over a two-year period. The research focuses on tracking key air pollutants, namely carbon monoxide (CO), nitrogen dioxide (NO₂), particulate matter (PM2.5 and PM10), and sulfur dioxide (SO₂), providing a comparative assessment of seasonal trends and the sources of pollution, which include transportation, industrial emissions, and domestic heating during the cold season. The study emphasizes the significance of monitoring systems in urban environments to understand better the impact of air pollution on public health and the effectiveness of sustainable interventions. One of the major insights from this research is the comparison between seasonal variations in pollutant levels and the city’s transition toward sustainable energy practices, such as increased gasification and the use of electric transportation, which has already demonstrated a positive impact on reducing emissions during peak heating periods. The results show that while Astana has improved air quality, air pollution remains a concern, especially in winter due to the increased use of solid fuel. This paper emphasizes the importance of real-time data from mobile sensors and suggests their wider use to complement stationary sensors for better monitoring. In addition to pollutant tracking, the study delves into the health implications of prolonged exposure to air pollutants, particularly in urban areas. The study concludes by advocating for expanded use of mobile monitoring systems and advanced data analytics to provide actionable insights for policymakers, urban planners, and public health officials. [ABSTRACT FROM AUTHOR]

The dilemma between economic development and environmental protection has gained widespread attention, but limited research from the perspective of political business cycles exists. By utilizing detailed official turnover datasets and two-way fixed effect models, this study investigates the impact of political turnovers of city leaders on changes in urban PM2.5 (particulate matter 2.5) density in China. It examines how different stages of a leader's tenure contribute to these changes and considers their potential connection to the performance-based cadre evaluation system that drives government turnovers in China. The study finds that prior to turnover, mayors tend to relax environmental regulations, leading to an 1.5% increase in PM2.5 density. However, during the second years after the mayor has taken office, air quality improves, with a 1% decrease in PM2.5 density. The effect of political business cycles on air quality is heterogeneous across cities with varying financial conditions, innovative capacity, and population size. The findings provide evidence that environmental protection might be sacrificed for economic development in the period prior turnover, particularly in cities with lower development levels. These insights can assist the government in considering how political cycles and the heterogeneous regional characteristics can be adjusted to promote environmental protection. [ABSTRACT FROM AUTHOR]

Air pollution is a common threat to all major cities worldwide for its harmful effects on human health, economy and sustainability. Occasionally, air quality is further degraded by several human activities and events. Particularly, the National Capital Territory (NCT) of Delhi, India, is susceptible to the after-effects of firecrackers bursting during Diwali, the festival of lights. Coincidentally, these effects are further accentuated by the crop residue burning prevalent in the adjoining states. In this study, the combined impacts of Diwali and stubble burning are examined around Delhi area. Spatio-temporal behavior of air pollutants, PM2.5 and PM10 (Particulate Matter with diameter ≤ 2.5 μ m and ≤ 10 μ m, respectively), is investigated on a weekly scale using geostatistical approach for the pre-COVID years of 2018 and 2019 to unravel short term trends, and the cause-effect relationships are explored. For both the years 2018 and 2019, North-West Delhi recorded, on an average, higher weekly mean concentrations of PM2.5 by 10.5 % and 4.25 % , and PM10 by 13.11 % and 4.94 % respectively, compared to other districts. Further statistical analysis reveals that the total number of weekly crop-burning instances is correlated to the weekly mean PM levels. In order to get finer microscopic view, spatio-temporal trends of PM values are thoroughly examined on daily and hourly scale for the Diwali Week in Delhi for both the years by analyzing the effects of firecrackers bursting and stubble burning. Backward trajectory analysis indicates occurrences of pollutants transportation from neighbouring states. [ABSTRACT FROM AUTHOR]