Pollutant Concentrations Research Articles

Pyrolysis of industrial hemp biomass from contaminated soil phytoremediation: kinetics, modelling transport phenomena and biochar-based metal reduction

Phytoremediation is the use of vegetation for the in situ treatment of contaminated environments. After plants have been used for phytoremediation of soils, their biomass can be used for example as value-added products or converted by thermochemical processes. Large-scale application of pyrolysis technology for phytoremediation biomass requires accurate predictive kinetic models and a characterization of the toxicity of the materials produced. The pyrolysis of industrial hemp (Cannabis sativa L.) was investigated on a laboratory scale by varying the process conditions and accurately modelled by considering four pseudo-components with first reaction order. The average value of the coefficients of determination is 0.9980. Biomass and biochar were characterized and the main components of the gas phase were monitored. We found Cd, Pb, and Zn in the roots, although in lower amounts than in the soil. Especially the leaves and stems showed negligible traces of these elements, so that these parts can be used directly, even if the hemp was grown on the polluted soil. After pyrolysis, the concentration of pollutants in the solid fraction decreased, which could be attributed to the reduction of metal oxides (or salts) to elemental form and subsequent evaporation. This pyrolysis process has the potential to treat heavy metal-rich biomass, with gas phase purification via condensation, yielding agricultural-grade biochar, CO-rich gas and a highly concentrated heavy metal stream in absorbent material.

Explainable deep learning on multi-target time series forecasting: an air pollution use case

Urban air pollution represents a significant threat to public health and the environment, with nitrogen oxides, ozone, and particulate matter being among the most harmful pollutants. These contribute to respiratory and cardiovascular diseases, particularly in urban areas with high traffic and elevated temperatures. Machine learning, especially deep learning, shows promise in enhancing the prediction accuracy of prediction of pollutant's concentrations. However, the “black box” nature of these models often limits their interpretability, which is crucial for informed decision-making. Our study introduces a Temporal Selection Layer technique within deep learning models for time series forecasting to tackle this issue. This technique not only improves prediction accuracy by embedding feature selection directly into the neural network, but also enhances interpretability and reduces computational costs. In particular, we applied this method to hourly concentration data of pollutants, including particulate matter, ozone, and nitrogen oxides, from five urban monitoring sites in Graz, Austria. These concentrations were used as target variables to predict, while identifying the most relevant features and periods that affect prediction accuracy. Comparative analysis with other embedded feature selection methods showed that the Temporal Selection Layer significantly enhances both model effectiveness and transparency. Additionally, we applied explainable techniques to evaluate the impact of weather and time-related factors on air pollution, which also helped assess feature importance. The results show that our approach improves both prediction accuracy and model interpretability, leading finally to more effective pollution management strategies.

Integrated impacts of surrounding building density and gap permeability on pollutant dispersion in four-way street networks.

Gaps between buildings facing the streets can effectively accelerate the natural removal of pollutants in street canyons by enhancing ventilation and diffusion processes. This removal process is closely related to gap permeability (Pg, ratio of gap width to street length) and building density (λn) surrounding the streets. However, the integrated effects of these two parameters on pollutant dispersion remain unclear, because of the limited computational resources and the difficulty of traditional modeling methods to discrete the numerous buildings. Numerical simulations coupled with the porous media approach are performed for this purpose. Results indicate that for isolated streets surrounded by empty land (λn = 0), the high-speed airflow through the gaps causes significant flushing effects on pollutants in the street, and thus larger Pg induces better street air quality than the case without gaps (Pg = 0). Conversely, when the streets are surrounded by buildings (λn ≠ 0), the air quality is good for both smaller Pg = 0 and larger Pg = 52 %, and the worst case occurs in Pg = 24 %. Moreover, because of the blocking effect of surrounding buildings, the percentage of pollutant removal by the gaps decreases with increasing λn, which can reach up to 50 % for λn = 0, decreasing to 30 % for medium density (λn = 0.22). Notably, for high density (λn = 0.44), only when Pg > 52 % the pollutant concentration can be reduced compared to Pg = 0. The present findings can serve as principles for determining appropriate building gap layouts at different building densities, thereby improving the urban air quality more effectively.

Fulvic acid removal from landfill contaminated groundwater by a permeable reactive barrier: from laboratory to field-scale analyses

Permeable reactive barriers (PRBs) show great potential for remediating groundwater polluted by leachate from municipal solid waste landfills (MSWLs). In this study, we conducted a series of batch tests to investigate the effects of environmental factors (e.g., temperature, pollutant concentration, and pH in groundwater) and the composition of PRB materials (i.e., Fe and zeolite) on fulvic acid (FA) removal and also simulated PRB filled with different composition to investigate its effectiveness. The response surface method (RSM) was applied to optimize experimental conditions for single- and double-stage adsorption/desorption processes. Experimental findings demonstrated that an adsorbent mixture of 2.900g Fe and 1.505g zeolite achieved the highest adsorption efficiency of 67.22% among four tested combinations. The double-stage adsorption process significantly enhances the FA removal efficiency compared to the single-stage process. Field-scale simulations indicated that the 1.0-m thick PRB developed in this study may achieve an FA adsorption efficiency of 67.31%. Moreover, the implementation of PRB may lead to a significant reduction in downstream FA concentrations, with a decrease of 40.90% compared to cases where only cut-off walls are employed. Therefore, the proposed Fe-Zeolite PRB system may serve as an alternative method to remedy polluted groundwater by landfill leachate.

Metabolomic profiling identifies signatures and biomarkers linking air pollution to dementia risk: a prospective cohort study

Exposure to air pollution has been associated with increased dementia. However, it remains unknown what specific metabolic mechanisms play a role in this relationship. We included 192,300 dementia-free participants from the UK Biobank cohort study. Annual concentrations of air pollution were assessed based on the residential address. Elastic net regression was performed to identify air pollution-related metabolites, and metabolic score was constructed. Cox regression models and covariate balancing generalized propensity scores (CBGPS) regression models were conducted to explore the longitudinal associations between air pollution/metabolic signatures and dementia risk. The underlying mechanisms between air pollution and dementia driven by metabolic signature or specific metabolites were also investigated. A total of 2,592 incident dementia cases were documented. We identified the metabolite profiles in response to air pollution exposure, including 87 metabolites for PM2.5, 65 metabolites for PM10, 76 metabolites for NO2, and 71 metabolites for NOx. The air pollution-related metabolic signatures were associated with increased risk of dementia, with hazard ratios (HR) of 1.17 (95% CI: 1.12, 1.22), 1.06 (95% CI: 1.02, 1.11), 1.16 (95% CI: 1.10, 1.21), and 1.17 (95% CI: 1.12, 1.22) for PM2.5, PM10, NO2 and NOx, respectively. The associations persisted using causal models. Metabolic signatures mediated the associations between air pollution exposure and dementia risk, with mediation proportions ranging from 6.57% to 12.71%. Additionally, we observed that a metabolite known as free cholesterol in medium VLDL (M-VLDL-FC) played a crucial mediating role. Our study provides novel insights into the metabolic mechanisms linking air pollution exposure to dementia risk.

Concentration of particulate matter and atmospheric pollutants in the residential area of Kathmandu Valley: a case study of March–April 2021 Forest fire events

Forest fires have become more intense and frequent in recently changing climates. The wide variety of pollutants released by forest fire include greenhouse gases, photochemically reactive compounds, and fine and coarse particulate matter. This study investigated the impact of forest fire events on air quality in the Kathmandu Valley during March-April 2021 using ground air quality monitoring stations and satellite data. The three fire periods were studied (a) Pre-fire from 21st - 23rd March (b) first-fire episode from 24th -27th March and (c) second fire episode from 1st – 5th April of 2021. The concentrations of PM2.5 reached to maximum 199 μg/m3 during pre-fire period, 371 μg/m3 and 280 μg/m3 during first and second fire event respectively. The second fire episode had lower PM2.5 concentration despite higher fire counts (449) compared to the first episode suggesting influence of fire activities near to vicinity of Kathmandu valley during second fire episode. There was a two-day lag between the beginning of forest fire events and an increase in PM2.5 levels in Kathmandu. Satellite observation showed varying patterns for different pollutants. HCHO levels responded quickly to fire activity, while AOD and CO levels increased after a few days. Also, low wind speed, low temperature, and low relative humidity additionally elevated these pollutants in Kathmandu. This study emphasizes the extent of the impact of forest fires on air quality and the importance of considering meteorological and satellite data to understand the distribution of pollutants during such events.

Preparation and optimization of sulfur ferrous inorganic carbon composite filler for autotrophic denitrification nitrogen and phosphorus removal

Sulfur autotrophic denitrification (SAD) occurs without organic carbon sources, offering advantages in removing nitrogen pollutants from water with low carbon to nitrogen ratio. However, ensuring nitrate–reducing sulfide–oxidizing bacteria ability to access the necessary sulfur and inorganic carbon sources is a challenge. Therefore, this study investigated the feasibility of utilizing a SAD composite filler to mitigate nitrogen and phosphorus pollutants concentrations in secondary effluent of wastewater treatment plants (WWTPs) and reduce eutrophication risk in the receiving water. The use of paraffin optimized composite filler with a satisfying 3.70 % break rate and wear rate without dramatically deteriorating contaminant removal performance. The process achieved 94.26 % total nitrogen (TN) and 90.91 % PO43--P removal rates in treating synthetic wastewater; and 2.72±1.92 and 0.29±0.06 mg/L of TN and PO43--P discharge in treating WWTPs secondary effluent, respectively. The results indicated that denitrification performance of the filler was primarily influenced by variations in NH4+-N resulting from SAD and dissimilatory nitrate reduction to ammonia caused by differences in filler composition and preparation factors. Based on the performance difference in SAD, Fex+ leached by H+ in the filler changed, affecting phosphorus removal performance. The change in mechanical properties of the filler was primarily dependent on the surface characteristics of the filler and the content/type of the binder. This study demonstrates the feasibility of using SAD for advanced nitrogen and phosphorus removal from wastewater and applying sulfur/siderite integrated composite filler as a pilot, thereby offering insights for the preparation of SAD fillers.

UTILIZATION OF CASSAVA PEEL AND CHITOSAN FOR THE PRODUCTION OF HYDROGEL AS A SUSTAINABLE SOLUTION FOR DOMESTIC WASTEWATER TREATMENT

Water pollution is a significant environmental issue, especially in developing countries where domestic wastewater often contains a high concentration of pollutants, posing risks to public health and the environment. Hydrogel from cassava peel and chitosan shows promise as a bioadsorbent for domestic wastewater treatment, but its capacity and optimal production temperature have not been fully studied. The study aimed to investigate the characteristics of hydrogel from cassava peel and chitosan, the optimal temperature for hydrogel production, and the efficacy of hydrogel as a bioadsorbent for domestic wastewater treatment. The study involved four stages: cellulose isolation, hydrogel preparation, wastewater treatment experimentation, and data analysis. The results showed that chitosan hydrogel synthesized at 750C had superior efficiency, achieving an optimal outcome of 88% in the treatment of household wastewater using a triad of chitosan hydrogel pellets. The degree of cross-linking in chitosan hydrogel diminished with an increase in heating temperature

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

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

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

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

Spatio-temporal analysis of bicyclists' PM2.5 exposure levels in a medium sized urban agglomeration.

Many cities have promoted decarbonized transportation modes to mitigate climate change, reduce air pollution and promote healthy behaviors. However, cyclists may be particularly exposed to higher concentrations of on-road air pollutants than other commuters due to their proximity to traffic, higher breathing rates, and prolonged commutes. In addition, there is scarce information analyzing the geographic exposure levels of cyclists in medium-sized urban agglomerations of Latin American cities. We aimed to assess cyclists' exposure to PM2.5 at the spatio-temporal level using low-cost sensors in the Mendoza Metropolitan Area, Argentina. We calculated PM2.5 inhalation doses (IDs) for different routes with distinctive characteristics, considering different age ranges and gender of cyclists. The dose was represented as temporally and spatially disaggregated exposure maps, one of the first to represent it in Latin America using this method. All link types analyzed exhibited significant differences in PM2.5 concentrations, although the most frequent concentrations were less than 5µgm-3, with secondary peaks of 6.5 and 9µgm-3. As expected, ID increases with age and is greater in males than in females. Our findings further reinforce the fact that route choice (busy vs. quiet) and time of day (peak vs. off-peak) appreciably affect the pollutant exposure of cyclists. All these results could be helpful in the selection of alternative cycling routes with lower PM2.5 at different hours of day but also enable us to investigate further implications of exposure to PM2.5 for the health of urban bicycle commuters.

A comparison of statistical and machine learning models for spatio-temporal prediction of ambient air pollutant concentrations in Scotland

AbstractThe spatio-temporal prediction of air pollutant concentrations is vital for assessing regulatory compliance and for producing exposure estimates in epidemiological studies. Numerous approaches have been utilised for making such predictions, including land use regression models, additive models, spatio-temporal smoothing models and machine learning prediction algorithms. However, relatively few studies have compared the predictive performance of these models thoroughly, which is one of the novel contributions of this paper. For the specific challenge of predicting monthly average concentrations of NO$$_{2}$$ 2 , PM$$_{10}$$ 10 and $$\hbox {PM}_{2.5}$$ PM 2.5 in Scotland, we find that random forests typically outperform (or are as good as) more traditional statistical prediction approaches. Additionally, we utilise the best performing model to provide a new data resource, namely, predictions of monthly average concentrations (with uncertainty quantification) of the above pollutants on a regular 1 km$$^{2}$$ 2 grid for all of Scotland between 2016 and 2020.

Instant and efficient greenly silver nanoparticles for remediating atrazine and methylene blue from contaminated water.

In an attempt to create economically feasible and sustainable wastewater treatment "green" techniques, Malva parviflora leaf water extract was used for biosynthesizing silver nanoparticles (Malva-AgNPs). Fourier transform infrared, ultraviolet-visible (UV-Vis), scanning electron microscopy, and dynamic light scattering (DLS) were used for the characterization of Malva-AgNPs. UV-Vis and DLS analysis revealed the stability of the Malva-AgNPs at a wavelength of 420nm and an average size of 100nm ± 1nm. A zeta potential of - 26.4mV provides additional support for the stability of the material. The removal studies were conducted using atrazine and methylene blue (MB) in a single or mixed liquid state. The adsorbent dose, pH, incubation time, and pollutant concentration in the adsorption process were investigated. The optimal removal for 500mg L-1 of atrazine and MB at the adsorbent dosage of 450mg, when incubated for 5min, was found to be 99.5% and 82.03% for atrazine and MB, respectively. Also, Malva-AgNPs eliminated more than 95% and 50% of the atrazine and MB mixture, respectively, in 5min. The kinetics study showed that the pseudo-second-order kinetics model was a better fit for explaining the experimental adsorption experiments for atrazine and MB. The obtained equilibrium adsorption data were examined using the Langmuir and Freundlich isotherm models, which indicate that atrazine and MB have maximum adsorption capacities of 434.78mgg-1 and 400mgg-1, respectively.

Modeling Land Use and Land Cover Changes and Its Atmospheric Pollutant Concentration in the Coal Mining Area of Ramgarh District of Jharkhand, India, Using Multi‐Layer Perceptron Neural Networks (MLPNN)

ABSTRACTLand use refers to anthropogenic phenomena in the natural environment; humans utilize land resources for their developmental activities. On the other hand, the ecosystems of land use and land cover alter the natural world—the artificial infrastructure leads toward a busted concrete jungle instead of a green footprint. The global green footprint is continually shrinking owing to overutilization of natural resources. The present research examines the land use pattern that changes from 1990 to 2021 and projected projections for 2041 and 2061 in the Ramgarh District. The study also focuses on how artificial modifications alter the concentration level of pollutants in the atmosphere. The Landsat data utilized for 1990, 2000, 2011, and 2021 were incorporated into the LULC map using supervised classification and for analysis of future predictions for 2041 and 2061 using an ANN‐based on MLPNNs (multi‐layer perceptron neural networks) for Ramgarh District. It also focuses on the trend and patterns of atmospheric pollutants from data using NASA‐GIOVANNI MERRA‐2. The current study reveals that in 1990, water bodies, coal mining, vegetation, built‐up, agriculture, and barren land were 3.01%, 2.24%, 54.07%, 3.64%, 36.85%, and 0.18 %. However, in 2021, water bodies decreased to 1.61%, vegetation to 45.47%, barren land to 0.65%, and an increasing tendency was observed in built‐up areas to 6.65%, coal mining to 2.43%, and farmland to 43.19%. A significant trend in atmospheric pollutants, such as CO2, SO2, SO4, NO2, and dust, is observed in the Ramgarh district. The importance of this study is to attain the maximum level of environmental sustainability; it would also encourage the local level planning fitted during the extraction of natural resources.

Evaluation of Gender-Specific Variation in Lead-induced Nephrotoxicity in Wistar Rats

The kidney is sensitive to heavy metals because of its intensive metabolic activity and multiple functions namely those of excretion and pollutants concentration. The study aimed to evaluate the gender-specific variation in lead-induced nephrotoxicity in Wistar rats. 10 male and 10 female Wistar rats (180-220g) were each divided into 2 groups (n=5 each): Control (M), Lead alone (M), Control (F), Lead alone (F). Male and female rats of the experimental groups were administered a daily dose of 100 mg/kg/bw of lead acetate dispersed in distilled water for 21 days. All rats were anesthetized and sacrificed 24 hours after the last administration. Blood samples were collected via cardiac puncture for biochemical analysis, kidney tissues were harvested, homogenized, and analyzed for antioxidant enzymes (superoxide dismutase, catalase, glutathione peroxidase) and lipid peroxidation (measured by malondialdehyde levels). Results indicated a significantly (p<0.01) lower weight gain in the lead-only female (F) group compared to the lead-only male (M) group. Lead acetate exposure caused oxidative damage, evidenced by significantly reduced antioxidant enzyme levels and increased lipid peroxidation in both male and female groups, with the effects being more pronounced in females (p<0.05). Serum creatinine and Urea levels in the lead alone (M) and lead alone (F) group were increased when compared to their respective control groups (p<0.05) however serum creatinine was significantly (p<0.001) higher in lead alone (F) than lead alone (M). The electrolyte function increased significantly in this study (p<0.05) when compared with their control groups. Sodium ion in lead alone (F) increased significantly (p<0.01) when compared to lead alone (M). The study concludes that lead exposure induced nephrotoxicity in both male and female rats, but more significantly pronounced in females than the males. This increased severity may have been mediated by the higher lead-induced oxidative stress observed in the female rats compared to males.

Response Surface Methodology: A Review on Optimization of Adsorption Studies

Herein, we reviewed response surface methodology (RSM), a powerful statistical tool widely used in optimizing adsorption processes to remove synthetic dyes, toxic heavy metals, and phenols from wastewater. The widely used RSM models during optimization are the central composite design and Box-Behnken, which are second-order polynomial models. The models give a predictive insight into the number of experimental runs to be carried out. Furthermore, an in-depth overview of RSM and its application in optimizing various adsorption parameters, such as adsorbent dosage, initial pollutant concentration, contact time, and pH is discussed. In addition, RSM enables researchers to efficiently determine the optimal conditions for maximum pollutant removal. Lastly, the findings of this research highlight the potential of RSM as a valuable tool for optimizing adsorption processes and contributing to sustainable water treatment technologies.

Indoor and ambient influences on PM2.5 exposure and well-being for a rail impacted community and implications for personal protections

Abstract Background
Higher air pollution concentrations can be observed near rail networks, local and highway automobile corridors, and shipyards. Communities adjacent to such sources are disproportionately exposed to air pollution from these stationary and mobile sources. One such community is West San Bernardino in California, where households are feet away from the Burlington Northern Santa Fe intermodal facility and are impacted by activities that are estimated to continuously emit air pollutants due to 24/7 operation.
Objective
This study aimed to (1) quantify the impact of personal mobility and housing characteristics on daily PM2.5 exposures and well-being for West San Bernardino community members, and (2) develop individualized resilience plans for community collaborators to support future PM2.5 exposure reduction. 
Methods
Personal PM2.5 exposures were measured for community collaborators for seven consecutive days during three deployment periods: October 2021, January 2022, and March 2022. Indoor and ambient PM2.5 levels were also continuously measured for five households over six months using PurpleAir Classic monitors. Demographic and well-being data were collected upon recruitment and after each week of engagement, respectively. 
Results
Personal exposures in home microenvironments were highest near the railyard and lower farthest away from the railyard. Home exposures were 40% higher on average compared to non-home microenvironments. Household PM2.5 had a higher-than-expected average infiltration factor of 0.55, and indoor 98th percentiles across the households far exceeded a healthy level at an average of 165 μg/m3. Resilience plans featured summaries of personal data and recommendations for mitigating exposures. 
Significance
Results suggest that surrounding land use and residential building characteristics compound to worsen air pollution exposures beyond what is expected for exposures in non-industrialized areas. Findings prompt a call for stronger regulation, not only for emissions, but also for indoor air quality and zoning standards that specifically protect disproportionately impacted communities.

Nocturnal Ozone Enhancement Induced by Sea-Land Breezes During Summertime in Northern Coastal City Qingdao, China

This study investigated the influence of sea–land breezes on nocturnal spatial and temporal distribution of ozone (O3) and its potential effects on particulate nitrate formation in Qingdao, a coastal city in northern China. Observation campaigns were conducted to measure surface air pollutants and meteorological factors during a typical sea–land breezes event from 22 to 23 July 2022. A coherent Doppler lidar (CDL) system was employed to continuously detect three-dimensional wind fields. The results revealed that nocturnal ozone levels were enhanced by a conversion of sea–land breezes. Initially, the prevailing northerly land breeze transported high concentrations of O3 and other air pollutants from downtown to the Yellow Sea. As the sea breeze developed in the afternoon, the sea breeze front advanced northward, resulting in a flow of high O3 concentrations back into inland areas. This penetration of the sea breeze front led to a notable spike in O3 concentrations between 16:00 on 22 July and 02:00 on 23 July across downtown areas, with an average increase of over 70 μg/m3 within 10 min. Notably, a time lag in peak O3 concentration was observed with southern downtown areas peaking before northern rural areas. During this period, combined pollution of O3 and PM2.5 was also observed. These findings indicated that the nighttime increase in O3 concentrations, coupled with enhanced atmospheric oxidation, would likely promote the secondary conversion of gaseous precursors into PM2.5.

Perforated plate ventilation system and dynamics of infectious respiratory particle transmission

AbstractWith the removal of indoor pollutants and the assurance of air quality emerging as critical research topics, the optimization of the internal environment in offices, where people stay for extended periods, is essential for controlling the spread of infectious respiratory particles. Frequent movements of personnel and the operation of doors and windows within offices significantly impact the mechanisms of droplet transmission, warranting further investigation. This study employs computational fluid dynamics simulations to explore the droplet dispersion characteristics and pollutant removal efficiency of the simplified model of perforated plate ventilation system (PPVS) (the diameter of the air supply openings has been reasonably simplified and uniformly set to 0.02 m) in office settings, as well as the impact of dynamic door operation scenarios on droplet spread and concentrations in breathing zones. To optimize the ventilation system's pollutant removal efficiency, airflow velocities (2.86, 3.18, and 5.00 m/s) are varied, with simulations conducted at the optimal velocity of 3.18 m/s. The effects of continuous door operations, door‐opening directions (towards the office and towards the isolation room), and opening speeds (π/4, π/6, π/8, and π/10 rad/s) are also examined, revealing significant impacts on droplet spread. Results indicate that PPVS effectively reduces indoor pollutant concentrations at all tested airflow velocities, with the optimal speed identified as 3.18 m/s. Additionally, door‐opening direction and speed can significantly influence droplet spread. Opening doors towards isolation rooms at smaller angles (less than 30°) effectively reduces droplet concentrations in personnel breathing zones, thereby mitigating the risk of droplet transmission. Faster door‐opening speeds also contribute to lower droplet concentrations in these zones. This innovative study explores the impacts of PPVS and dynamic door operation dynamics on droplet transmission during respiratory disease outbreaks, providing valuable theoretical insights and technical support for disease prevention and indoor air quality improvement.

Measured air quality impacts after teaching parents about cooking ventilation with a video: a pilot study.

Cooking-related emissions contribute to air pollutants in the home and may influence children's health outcomes. In this pilot study, we investigate the effects of a cooking ventilation intervention in homes with gas stoves, including a video-based educational intervention and range hood replacement (when needed) in children's homes. This was a pilot (n = 14), before-after trial (clinicaltrials.gov #NCT04464720) in homes in the San Francisco Bay Area that had a school-aged child, a gas stove, and either a venting range hood or over-the-range microwave/hood. Cooking events, ventilation use, and indoor air pollution were measured in homes for 2-4 weeks, and children completed respiratory assessments. Midway, families received this intervention: (1) education about the hazards of cooking-related pollutants and benefits of both switching to back burners and using the range hood whenever cooking and (2) ensuring the range hood met airflow and sound performance standards. The educational intervention was delivered via a video developed in conjunction with local youth. We found substantially increased use of back burners and slight increases in range hood use during cooking after intervening. Even though there was no change in cooking frequency or duration, these behavior changes resulted in decreases in nitrogen dioxide (NO2), including significant decreases in the total integrated concentration of NO2 over all cooking events from 1230 ppb*min (IQR 336, 7861) to 756 (IQR 84.0, 4210; p < 0.05) and NO2 collected on samplers over the entire pre- and post-intervention intervals from 10.4 ppb (IQR 3.5, 47.5) to 9.4 (IQR 3.0, 36.1; p < 0.005). There were smaller changes in PM2.5, and no changes were seen in respiratory outcomes. This pilot before-after trial evaluated the use of a four-minute educational video to improve cooking ventilation in homes with gas stoves and one or more school-aged children. Participant behavior changed after watching the video, and there were decreases in indoor air pollutant concentrations in the home, some of which were significant. This brief video is now publicly available in English and Spanish (wspehsu.ucsf.edu/projects/indoor-air-quality), and this provides suggestive evidence of the utility of this simple intervention, which could be particularly beneficial for households that have children with asthma.