PM10 Concentrations Research Articles

Water-soluble organic compounds (WSOC) from atmospheric aerosols in Bou-Ismail (Algeria).

In this contribution, we report the study of nuclear resonance magnetic spectroscopy techniques (1H-NMR, 13C-NMR, and 2D-NMR) efficiency in the characterisation of the functional composition of water-soluble organic compounds (WSOC) from atmospheric aerosols. The chosen site was our scientific and technical center of research (CRAPC) situated in Algerian Bou-Ismail city. where the concentrations of PM10 were found to be between 15.66 and 142.19µg.m-3. As results, 1H-NMR analysis showed the coexistence of biological material and emissions from urban and biomass burning. The dominant source was identified by quantitative integration of each 1H NMR spectral region. By using the HSQC technique, many peaks are revealed in biogenic samples including biomass burning. On the other hand, the identification of the source of various organic compounds and their functional composition is possible through specific NMR spectra, which can also be used to adjust the surrounding organic aerosol sources.

Source contributions to two super dust storms over Northern China in March 2021 and the impact of soil moisture

Two extremely devastating super dust storms (SDS) hit Mongolia and Northern China in March 2021, causing many deaths and substantial economic damage. Accurate forecasting of dust storms is of great importance for avoiding or mitigating their effects. One of the most critical factors affecting dust emissions is soil moisture, but its value in desert exhibits significant uncertainty. In this study, model experiments were conducted to simulate dust emissions using four soil moisture datasets. The results were compared with observations to assess the effects of soil moisture on the dust emission strength. The Integrated Source Apportionment Method (ISAM) was used to track the dust sources and quantify the contribution from each source region to the dust load over the North China Plain (NCP), Korea peninsula, and western Japan. The results show large differences in the dust load depending on the soil moisture datasets used. The high soil moisture in the NCEP dataset results in substantial underestimation of the dust emission flux and PM10 concentration. Despite a minor overestimation of PM10 concentrations in many Northern China cities, the ERA5 dataset yields the best simulation performance. During the two SDS events, about 7.5 Mt dust was released from the deserts in Mongolia and 2.8 Mt from the deserts in China. Source apportionment indicates that the Mongolian Gobi Desert is the dominant source of PM10 in the NCP, Korea peninsula, and western Japan, accounting for 60 %–80 %, while Inner Mongolia contributed 10 %–20 %.

Effects on the Indoor Environment in a Stable for Horses in Winter: A Case Study

The aim of this article is to show the most significant factors influencing the indoor environment in winter considering the operating conditions of an older stable modified for housing 12 horses and an indoor riding arena for teaching and sports purposes. This research focused on assessing the influences affecting the internal environment from the point of view of the construction of the building and ventilation control in the operating conditions of working and non-working days. The analysis of the results showed that the massive masonry structure has sufficient thermal insulation and accumulation, which was manifested by good temperature stability inside the stable of 7.2 ± 1.7 °C when the outside air temperature was −4.80 ± 1.5 °C. At low outdoor air temperatures of −6.44 ± 0.4 °C, the following conditions were found: a higher relative air humidity (76.0 ± 5.3%), a high CO2 concentration 2317.1 ± 931.7 ppm, and a high airborne dust concentration PM10 = 231.94 ± 19.13 μg·m−3 and PM2.5 = 160.13 ± 6.28 μg·m−3. Therefore, it is necessary to improve the solution and function of the stable ventilation. The small size of the windows and their uneven distribution (average daylight factor ei from 0.313 ± 0.154 to 0.835 ± 0.309) caused insufficient daylight in some individual boxes.

A long-term Doppler wind LiDAR study of heavy pollution episodes in western Yangtze River Delta region, China

Previous studies are primarily based on surface air pollution for a single event, leaving research on the temporal characteristics of aerosols and wind fields in the atmospheric boundary layer (ABL) and their associations unclear due to a lack of long-term vertical profile data. This study was the first to conduct long-term Doppler wind LiDAR measurements from Sep. 2019 to Aug. 2022 in Hefei in western Yangtze River Delta (YRD) region, China. The spatiotemporal characteristics of retrieved aerosol backscatter coefficient (β) and wind profiles were analyzed from the perspective of long-term statistics and typical heavy pollution episodes (HPEs). The seasonal profile of β showed a peak in the near-surface layer meanwhile the overall β profile (<0.5 km) was the highest in winter but lowest in summer. Combined with ground-based meteorological and air quality observations, 12 HPEs were identified and classified into dust-related events and fog-haze episodes. The results showed a consistency between hourly variations in PM10 (particulate matter smaller than 10 μm) concentration and β retrieved at 300 m during a high PM10 concentration (>150 μg/m3) period. Different roles of horizontal wind at different altitudes and its associated time delay effect on surface PM10 pollution were evaluated based on correlation coefficients (Ф) and air pollution diffusion conditions. Significant positive Ф values were noticed below 0.5 km during the entire dust-related EP4 and EP6, indicating that the higher wind speeds could exacerbate PM10 pollution. In contrast, large negative Ф values meant the removal of PM10 pollutants by strong winds below 0.8 km during 24 h after peak in EP3. Combining with backward trajectory analysis and meteorological condition, notable transboundary pollution with positive contributions from upper winds (>1.5 km) was discovered in dust-related EP1, EP4, EP7, EP9, and EP10. Time delay effect (1-h/2-h lag) of upper winds (>0.8 km) on surface PM10 pollution was explored in EP5, EP6, EP7, EP8, and EP9. In spring dust-related HPEs, surface PM10 pollution was mostly contributed by long-range transport of aerosol particles at higher altitudes (>1.5 km) from the northwest direction, driven by the Mongolian cyclone and the cold front system. Transboundary aerosols originated from the northern part of Anhui province and the YRD region in the middle altitudes (∼0.5 km) was the main contributor to fog-haze HPEs. The findings demonstrated the ability of the real-time Doppler wind LiDAR system to monitor transboundary air pollution and provided a scientific reference for policy makers.

Seasonal variation of water-soluble inorganic ions and carbonaceous components of PM2.5 and PM1 in industrial and residential areas of Suizhou, China

Seasonal variations of fine particulate matter (PM) and its chemical components in Suizhou are investigated by collecting PM2.5 and PM1 samples from industrial and residential areas between March 2017 and February 2018. The findings reveal more severe PM pollution in industrial areas compared to residential areas. Concentrations and percentages of water-soluble inorganic ions (WSII) in PM are higher in spring and winter. Notably, the high levels of SO42− and NO3− in PM1-2.5 indicate that secondary transformation of SO2 and NOx significantly contribute to the rapid increase in PM1-2.5. The conversion of NO2 to NO3− is a key factor in the winter increase of mass concentrations of PM2.5 [ρ(PM2.5)], while photochemical reactions involving NO2 drive the summer increase of ρ(PM2.5). Organic carbon (OC) and elemental carbon (EC) are mainly distributed in PM1, with OC peaking in winter. Chemical fractionation of PM reveals that carbonaceous components have a greater impact on PM1 concentration than WSII, whereas WSII more significantly affects ρ(PM2.5) than carbonaceous components. The OC to EC ratio in industrial areas (2.5 ± 1.4) and residential areas (1.9 ± 0.8) suggests more prominent secondary aerosol pollution in industrial areas. Significant correlations between secondary organic carbon (SOC), SOC conversion rate (ηSOC), and concentrations of O3-8h and NO2 in Suizhou further indicate that O3 and NO2 levels in the atmosphere influence the generation of SOC and ηSOC.

Long-range transport impact of a severe dust storm over the Yangtze River Basin region and its modeling sensitivity to dust emission scheme

Dust storms frequently occur in the spring over East Asia, affecting southern Mongolia and northeast China. An exceptionally powerful dust storm occurred in East Asia from March 14th to March 18th, 2021. To investigate the ability of climate models to simulate severe dust storms, we employed the Weather Research Forecast model coupled with chemistry (WRF-Chem) with Goddard Chemistry Aerosol Radiation and Transport (GOCART), SHAO and KOK dust emission schemes to simulate the evolution of this event and to characterize dust aerosols long-range transport. Results showed that this storm was caused by a Mongolian cyclone depicted at 850 hPa with a cold front, which swept dust aerosols southeastward across southern Mongolia to northeast China. Model evaluation with satellite-retrieved Aerosol Optical Depth (AOD) and observational PM10 concentrations revealed that the SHAO dust emission scheme performed better in simulating spatial and temporal distribution of AOD over the Gobi Desert (GD) but overestimated them over Taklimakan Desert (TD). In contrast, the GOCART dust emission scheme underestimated AOD over most areas except the Taklimakan Desert; the KOK dust emission scheme overestimated them over both TD and GD. Overall, the SHAO dust emission scheme had good performance in simulating the surface PM10 concentrations over the Yangtze River Basin (YRB) and depicted an increase in PM10 concentrations, which denoted the transport of dust aerosols over YRB. As a better description of the physical process of dust emission flux over the eastern region of the Gobi Desert, the SHAO dust emission scheme depicted well the spatial and temporal evolution of dust plume episodes, especially over northeast China and YRB. The significant differences in dust emission fluxes between the three dust emission schemes were attributed to the sensitivity of threshold friction velocity and erodibility factor. Thereunto, the SHAO dust emission scheme incorporated well soil particle size distribution and threshold velocity to produce the dust emission flux, which allowed higher dust emission flux over the eastern GD and under strong southwesterly transported dust aerosols to East Central China's region of North China Plain (NCP) and YRB. This study will help further the simulation of extreme dust events more accurately and the exploration of climate change's effect on dust events over East Asia.

Spatial and temporal changes of air quality in Shandong Province from 2016 to 2022 and model prediction

This study investigates the spatial and temporal dynamics of air quality in Shandong Province from 2016 to 2022. The Air Quality Index (AQI) showed a seasonal pattern, with higher values in winter due to temperature inversions and heating emissions, and lower values in summer aided by favorable dispersion conditions. The AQI improved significantly, decreasing by approximately 39.4 % from 6.44 to 3.90. Coastal cities exhibited better air quality than inland areas, influenced by industrial activities and geographical features. For instance, Zibo's geography restricts pollutant dispersion, resulting in poor air quality. CO levels remained stable, while O3 increased seasonally due to photochemical reactions in summer, with correlation coefficients indicating a strong positive correlation with temperature (r = 0.65). Winter saw elevated NO2 levels linked to heating and vehicular emissions, with an observed increase in correlation with AQI (r = 0.78). PM2.5 and PM10 concentrations were higher in colder months due to heating and atmospheric dust, showing a significant decrease of 45 % and 40 %, respectively, over the study period. Predictive modeling forecasts continued air quality improvements, contingent on sustained policy enforcement and technological advancements. This approach provides a comprehensive framework for future air quality management and improvement.

Evaluating Indoor Air Quality in Schools: Is the Indoor Environment a Haven during High Pollution Episodes?

Pollution data were collected at five schools in Hong Kong using low-cost, sensor-based monitors both indoors and outdoors during two consecutive high pollution episodes. The pollutants monitored included NO2, O3, PM2.5, and PM10, which were also used as input to a health risk communication protocol known as Air Quality Health Index (AQHI). CO2 was also measured simultaneously. The study aimed to assess the relationship between indoor pollutant concentrations and AQHI levels with those outdoors and to evaluate the efficacy of building operating practices in protecting students from pollution exposure. The results indicate that the regular air quality monitoring stations and outdoor pollutant levels at schools exhibit similar patterns. School AQHI levels indoors were generally lower than those outdoors, with PM10 levels showing a larger proportional contribution to the calculated values indoors. NO2 levels in one school were in excess of outdoor values. CO2 monitored in classrooms commonly exceeded indoor guidelines, suggesting poor ventilation. One school that employed air filtration had lower indoor PM concentrations compared to other schools; however, they were still similar to those outdoors. O3 levels indoors were consistently lower than those outdoors. This study underscores the utility of on-site, sensor-based monitoring for assessing the health impacts of indoor and community exposure to urban air pollutants. The findings suggest a need for improved ventilation and more strategic air intake placement to enhance indoor air quality.

On the suitability of dispersion models of varying degree of complexity for air quality assessment and urban planning

The development of integrated urban services requires the implementation of informative tools that provide a balance between quality, time and costs for air quality assessment. Within this framework, three modeling techniques with different levels of complexity were compared during a winter inversion episode against PM10 concentrations measured in a built-up area in Prague (Czech Republic) characterized by heavy traffic. Although the Gaussian model ATEM satisfied the common statistical-performance criteria, the predictions poorly represented the spatial variability of concentrations in the study domain. The Lagrangian model GRAL provided a better simulation of the effects of terrain and vortice formation inside street canyons, but tended to overpredict the influence of these phenomena. Finally, the most sophisticated of the three models, the Large-Eddy Simulation model PALM, demonstrated the best performance based on an exhaustive analysis of the model outputs in the temporal and spatial dimensions. After model comparison, a sensitivity test of the selected models to the driving meteorology and emissions inputs was carried out. While advanced models can simulate complex urban environments, their suitability for use in urban planning is subject to further considerations, such as computational cost, user expertise, and the usefulness of the output. Thanks to increasing computation power and intensive work on the entire modeling chain, sophisticated models could become routine tools for use in regulatory applications, contributing to future integrated urban service provision.

Electrospun poly (butylene adipate-co-terephthalate)/polydimethylsiloxane/carbon nanotubes nanofiber membranous pressure sensor for monitorable air purification and breathing detection

This research uses electrospinning to present a method for producing a pressure- and breath-responsive nanofiber membrane (PBAT/PDMS/CNTsNFM). This method utilizes polydimethylsiloxane (PDMS) and poly (butylene adipate-co-terephthalate) (PBAT), which is biodegradable, as the matrix, along with carbon nanotubes (CNTs) as a conductive filler. Additionally, a combination approach was proposed incorporating cellulose non-woven fabric (CNWF) as a support and initial filtration layer, serving as a collector for electrospinning PBAT/PDMS/CNTsNFM to create a twin-layer structure. In rheology analysis, the viscosity of PBAT/PDMS/CNTs solution was 0.811 Pa·s at the shear rate of sample preparation. PBAT/PDMS/CNTs solution performed 30.14 mN/m in the surface tension investigation. The formation mechanism of PBAT/PDMS/CNTsNFM was analyzed, and CNTs aid in forming fibers in PBAT/PDMS/CNTsNFM. PBAT/PDMS/CNTsNFM exhibits an average fiber diameter of 370 nm. In the air purification performance test (with PM0.3, 85 L/min air flow rate, 8 mg/m3 PM concentration), the 7.62 g/m2 PBAT/PDMS/CNTsNFM sample performed an air purification efficiency of 87.5 % and a pressure drop of 225 Pa, and the 22.3 g/m2 PBAT/PDMS/CNTsNFM sample performed an air purification efficiency of 92.4 % and a pressure drop of 1959 Pa. In the pressure detection test, the resistance of the 22.3 g/m2 PBAT/PDMS/CNTsNFM sample decreased as the pressure increased, and the response can be observed even at a low pressure of 25 Pa. The 7.62 g/m2 PBAT/PDMS/CNTsNFM sample could respond to breath with different strengths. The CNTs form a pressure-constructed dynamic conductive network in PBAT/PDMS/CNTsNFM. PBAT/PDMS/CNTsNFM is expected to be applied to pressure sensing, monitorable air purification, and breathing detection due to its performance in pressure detection and air purification.

Association between air pollution, meteorological factors and hand, foot, and mouth disease incidence in Zhejiang Province: A Bayesian spatio-temporal modelling study

Temperature and humidity are known to influence seasonal patterns of hand, foot and mouth disease (HFMD) transmission. However, the effect of air pollution and climate-air pollution interaction on the intensity of HFMD epidemics is poorly understood. This study aims to quantify the exposure-response effects of air pollution on HFMD risk by level of meteorological indicators in Zhejiang Province. A spatiotemporal Bayesian hierarchical model was used to determine the exposure-response association between air pollution, meteorological factors and the relative risk (RR) of HFMD. The model was fitted to monthly HFMD case data for the 92 counties in Zhejiang Province between January 2013, and December 2021. We assessed both linear interaction effects between air pollutants and meteorological factors on HFMD, and the modification role of extreme weather conditions on the nonlinear air pollutant-HFMD association. A 10 μg/m³ increase in PM10 and NO2 concentration raised the HFMD risk by 6.6% (RR = 1.066, 95% CI: 1.042, 1.092) and 20.8% (RR = 1.208, 95% CI: 1.162, 1.256) respectively. HFMD was negatively associated with O3 (RR = 0.980, 95% CI: 0.968, 0.993), SO2 (RR = 0.820, 95% CI: 0.776, 0.868) and CO (RR = 0.770, 95% CI: 0.621, 0.956). Temperature, humidity, and precipitation positively interacted with most air pollutants on HFMD, while wind speed had a negative interaction effect. In higher temperature conditions, the HFMD risk associated with PM10 increased. Elevated humidity levels amplified the risk associated with PM10, as did CO concentrations within a certain range. Heavy rainfall intensified the risk of HFMD associated with PM10, SO2, and CO. Results of this study showed that air pollution was associated with HFMD incidence, with different climate factors playing various modification roles in the associations and primarily leading to increased risk under extreme conditions. These findings provide a theoretical basis for personal protection and government prevention and control measures.

Airborne particulate matter integral assessment in Magdalena department, Colombia: Patterns, health impact, and policy management

The relevance of atmospheric particulate matter (PM) to health and the environment is widely known. Long-term studies are necessary for understanding current and future trends in air quality management. This study aimed to assess the long-term PM concentration in the Magdalena department (Colombia). It focused on the following aspects: i) spatiotemporal patterns, ii) correlation with meteorology, iii) compliance with standards, iv) temporal trends over time, v) impact on health, and vi) impact of policy management. Fifteen stations from 2003 to 2021 were analyzed. Spearman-Rho and Mann-Kendall methods were used to correlate concentration with meteorology. The temporal and five-year moving trends were determined, and the trend magnitude was calculated using Teil-Sen. Acute respiratory infection odd ratios and risk of cancer associated with PM concentration were used to assess the impact on health. The study found that the maximum PM10 concentration was 194.5 μg/m3, and the minimum was 3 μg/m3. In all stations, a negative correlation was observed between PM10 and atmospheric water content, while the wind speed and temperature showed a positive correlation. The global trends indicated an increasing value, with five fluctuations in five-year moving trends, consistent with PM sources and socio-economic behavior. PM concentrations were found to comply with national standard; however, the results showed a potential impact on population health. The management regulation had a limited impact on increasing concentration. Considering that national regulations tend to converge towards WHO standards, the study area must create a management program to ensure compliance.

Mapping air quality variations in industrial and agricultural zones: Understanding spatial and temporal patterns of selected air pollutants

With the escalating threat of air pollution on the biota, the need for research on pollutants has become imperative in industrial, urbanized and heavily populated cities. This study aimed to determine the level of major outdoor atmospheric pollutants (PM10, PM2.5, TVOCs, and HCHO) in specific locations including industrial and agricultural zones. Field measurements for this research, spanning 52 locations, were collected in the spring season using a portable instrument, and spatial variation maps were developed using ArcGIS software. The study measured PM10 concentrations ranging from 31 μg/m3 to 267 μg/m3 and PM2.5 concentrations ranging from 27 μg/m3 to 253 μg/m3. PM2.5 levels at most sites exceeded USEPA, WHO, and PEQS standards, while PM10 levels generally met these standards. The highest values of coarse and fine particulate matter were observed in areas dominated by emissions from the textile industry. Furthermore, particulate matter concentrations showed spatial variations, increasing towards the North and East directions. The Total Volatile Organic Compounds (TVOCs) concentrations, ranging from 0 mg/m3 to 4.019 mg/m3, exceeded Hong Kong SAR standards at five locations, thereby elevating health risks for residents in these areas. Similarly, formaldehyde levels, ranging from 0 mg/m3 to 0.754 mg/m3, surpassed the Hong Kong SAR standard at seven locations, indicating a substantial threat of exposure for the local population. In general, air quality at most field sampling sites was registered as UNQUALIFIED but was also observed to be FRESH at a few locations. This research is significant for understanding the levels of major pollutants so that the concerned authorities can take appropriate measures to tackle air pollution by introducing pollution control technologies in industries, switching to renewable energy options, and managing road dust.

Particle dispersion in atmospheric modelling: A comprehensive review

Atmospheric dispersion modeling, traditionally inclined towards gaseous dispersion, has undergone significant evolution in capturing the intricacies of particle dispersion in urban and open environments. This comprehensive review explores the nuances of particle-gas interactions, highlighting discrepancies in correlations between their concentrations, influenced by factors such as turbulence and multiple emission sources. The research accentuates the intriguing dynamics between PM2.5 and PM10 concentrations, suggesting the viability of models based on passive scalars for such particles in open environments. However, a marked challenge emerges in modeling particle number concentration, necessitating the integration of aerosol dynamics modules. Emphasizing the diversity of model types, this paper elucidates the specific requirements across varying spatial scales, identifying gaps in understanding particle dispersion and aerosol dynamics. The review critically assesses the performance of notable models, highlighting the paramount importance of quality data sources and underscoring the need for more dedicated focus on particle dynamics beyond mass predictions. Through a synthesis of existing literature and model evaluations, this review seeks to guide future research endeavors, fostering advancements in atmospheric dispersion modeling.

Half-metallic ferromagnetism with high critical temperatures in Substitutionally Doped Rare-Earth 2D Germanene

This work delves deep into the exploration of Rare-earth (RE) replacement doping germanene monolayers, where RE elements stand out for their fascinating electronic and magnetic nature derived from the elusive f-orbitals. Employing density functional theory (DFT) calculations, we unveil the electronic and magnetic characteristics of doped germanene systems. With its two-dimensional honeycomb structure, germanene holds immense promise for a wide range of applications within group IV materials. Through the discussion, we deeply uncover how the substitution of two specific concentrations of RE-atoms, namely La, Ce, Pr, Nd, Sm and Pm, transforms semi-metallic non magnetic germanene to half-metallic ferromagnets with remarkably high critical temperatures, when Hubbard correction is considered. These revelations not only deepen our comprehension of integrating f-orbitals into germanene but also open up exciting avenues for advancements in spintronic devices and beyond.

Sources of PM10 ionic species in the South-West Mediterranean (Algeria).

The contents of water-soluble major's ions (MSA, Cl-, NO3-, SO42-, Na+, NH4+, K+, Mg2+, and Ca2+) in the PM10 particle fraction were investigated thanks to detailed measurements of the main chemical constituents of PM10 in remote coastal areas in Bou-Ismail; in the South-West of the Mediterranean Sea (Algeria), during a 2-year period; from July 2011 to August 2013, under the framework of the ChArMEx project (Chemistry-Aerosol Mediterranean Experiment, http://charmex.lsce.ipsl.fr ). The total water-soluble ion concentrations in PM10 at the Bou-Ismail measurement station varied from 3.3µg/m3 (July 2011) to 49.6µg/m3 (March 2012). The annual mean mass concentrations of ions in the PM10 particulate fraction were Cl- > Na+ > SO42- > Mg2+ > NO3- > Ca2+ > K+ > NH4+ > Oxalate. The change in potassium nss-K + concentrations in PM10 over the course of a year reveals that biomass burning (BB) has an effect on three separate seasons: the beginning of winter (February and March), the end of summer (August), and the autumn (September and October). The origin periods of biomass burning BB identified employing the mapping of hotspots and fires during periods of August and September 2011, 2012, and 2013 underlined the important fires in the surrounding areas of the Mediterranean Sea (Sardinia Islands from Italy, Corsica from France).

Patterns and disparities in indoor particulate matter levels in selected primary schools in Kigali, Rwanda

Air pollution is a global environmental and public health challenge. There is limited evidence about the air quality in Rwanda, and the concentrations of particulate matter (PM), namely PM2.5 and PM10 in schools have not been well documented. This study evaluated patterns and disparities in indoor PM levels in selected primary schools in Kigali, Rwanda.The study collected PM2.5 and PM10 concentrations from six classrooms in six selected primary schools during the regular school study period in the dry season. Data were collected using mobile air sensors (purple air/PA–II–SD air quality) and an observation checklist. A Kruskal-Wallis test was performed to assess the difference in PM2.5 and PM10 concentrations between the six schools. The post-hoc Mann-Whitney test was used to compare all group pairs.The results indicated a significant difference in both the indoor PM2.5 concentration (H (5) = 41.01, p < 0.001) and the indoor PM10 concentration (H (5) = 38.5, p < 0.001). The maximum concentration observed was 133.6 μg/m3 for PM2.5 and 158.5 μg/m3 for PM10. Schools in highly exposed areas tended to have higher concentrations of PM than schools in moderately exposed areas. Specifically, the daily average concentration of PM2.5 in schools located in highly exposed areas ranged from 39 μg/m³ to 118 μg/m³, while PM10 levels ranged from 44.0 μg/m³ to 126 μg/m³. In contrast, schools in moderately exposed areas had daily PM2.5 average concentrations ranging from 32.0 μg/m³ to 111.0 μg/m³ and daily PM10 average concentrations ranging from 38.0 μg/m³ to 119 μg/m³.Overall, the recorded values for both PM2.5 and PM10 in all sampled schools were higher than the World Health Organization air quality guidelines. Indoor air quality is poorer in schools situated in highly exposed areas. This study suggests interventions to improve school air quality for the benefit of school communities.

Assessment of ambient air quality of Lucknow city post monsoon 2023

This study monitoring and assessment of air pollutants, including lead and nickel trace metals, gases such as sulphur dioxide (SO2) and nitrogen dioxide (NO2), and respirable suspended particulate matter (PM10) and fine particulate matter (PM2.5), were done at nine locations that were divided into three categories: residential, commercial, and industrial of the ambient air quality in the city of the post-monsoon. With an average of 148.9 µg m-3, the 24-hour concentrations of PM10 ranged from 58.5 to 337.8 µg m-3, whereas the 24-hour concentrations of PM2.5 ranged from 32.5 to 118.4 µg m-3, with an average of 70.5 µg m-3. At every location, the mean concentrations of PM10 exceeded the allowable threshold of 100 µg m-3. While PM2.5 levels, with the exception of Aliganj, Vikas Nagar, and Chowk, exceeded the 60µg m-3 national limit. Pb and Ni, which are PM10 trace elements, range from 0.0028 to 0.0370 µg m-3, with an average of 0.0147 µg m-3 for Pb and 1.30 to 11.75 µg m-3, with an average of 6.62 µg m-3 for Ni. During a 24-hour period, the levels of SO2 varied between 7.9 and 57.7 µg m-3, with an average of 20.8 µg m-3. Conversely, the levels of NO2 varied between 9.3 and 89.0 µg m-3, with an average of 37.8. Between µg m-3 the post-monsoon of 2022 and 2023, SO2 concentrations declined by 16.0% and NO2 values increased by 22.0%, respectively. On the other hand, average SO2 and NO2 levels were significantly lower than the 80µg m-3permitted limits set by the CPCB (NAAQS-2009).

Topographic shielding and funneling for dust events associated with the North American Monsoon in the Sonoran Desert, central Arizona, USA

Dust storms are linked to large scale accidents and fatalities in the Central Sonoran Desert, specifically in the central Gila River Valley of the United States during the North American Monsoon. This study analyzes three events where the underlying topography impacts large summertime dust storms and their movement. All three examples demonstrate the shielding or, conversely, funneling effects associated the underlying terrain. The funneling between isolated mountain complexes, such as White Tank Mountains, the Sierra Estrella, the Sacaton Mountains, the San Tan Mountains, and the South Mountain Complex can combine to create substantially higher dust concentrations. Conversely, dust flow into these isolated mountain complexes can also lead to shielding, or blocking, of the dust such that area behind the topographic features experiences less dust while the windward side of the barriers have accented dust concentrations. These dust storm/topography studies provide a first opportunity to identify topographic influences on central Arizona dust storms. Significance statementThe purpose of this study is to identify Topographic elements of the central Sonoran Desert located in the Arizona region of the Southwestern United States that impact the movement of dust storms. Using IDW interpolation to analyze PM10 concentration levels and wind speeds, we find that stand-alone mountain complexes can provide significant dust shielding and funneling that effect the overall trajectory of a dust storm in this region.

Drivers of High Concentration and Dispersal of PM10 and PM2.5 in the Eastern Part of Chhota Nagpur Plateau, India, Investigated Through HYSPLIT Model and Improvement of Environmental Health Quality

ABSTRACTThe ambient air quality is continuously declining especially in emerging countries because of rapid rate of urbanization, industrialization, and allied activities. Thus, the principal objective of this research is to inspect the spatiotemporal variation of particulate matters and to identify the drivers of pollutant dispersal. The month‐wise PM10 and PM2.5 data of 2022 and 2023 were obtained from the West Bengal Pollution Control Board. Meteorological datasets such as wind speed (m/s) and wind direction (degree) were collected from the NASA power access website. The HYSPLIT (Hybrid Single‐Particle Lagrangian Integrated Trajectory) model was applied to identify the pollutant's trajectories. Results showed that Purulia station stood highest in case of absolute AQI 715 (23‐1‐2022 at 7 a.m.), PM2.5 483.47 µg/m3 (23‐1‐2022 at 8 a.m.), and PM10 682.28 µg/m3 (23‐1‐2022 at 7 a.m.), respectively. During winter, the vertical expansion of particles was within the 100‐m boundary layer while during summer, it dispersed vertically and crossed 2000 m above the ground surface due to the combined effect of atmospheric conditions such as wind speed, wind direction, air pressure, and temperature. Moreover, the industrial outputs of Jharkhand and West Bengal play a significant role in elevating the pollution level. Plantation of suitable pollution‐resistant plants including Banyan (Ficus bengalensis), Mango (Mangifera indica), Peepul (Ficus religiosa), Guava (Psidium guajava), Kadam (Neolamarkia cadamba), Neem (Azadirachta indica) is a suitable management technique against pollution in this region.