Search

This paper explores the enduring impact of freeway construction on urban neighborhoods and communities, focusing on Los Angeles. The legacy of freeways in Los Angeles, often and purposefully constructed through marginalized communities during the mid-20th century for the benefit of white suburbanites, is reflected in significant social, economic, and environmental symptoms today. This paper also examines de-freeway methods to reconnect these divided communities, assessing their need and effectiveness through data analysis and case studies.The case studies presented in this paper offer a diverse range of solutions. They include the capping of I-5 for Freeway Park in Seattle, the tunneling of the Central Artery for Boston's Big Dig, the activation of I-5 underpasses for Chicano Park in San Diego, and the creation of the Ricardo Lara embankment park along the I-105 freeway in Lynnwood. These examples provide valuable insights into the processes, challenges, and successes of de-freeway and community reconnection efforts, showcasing the adaptability of these solutions in different urban contexts.The paper introduces three design proposals for freeway sites across six communities in Los Angeles. These proposals are guided by urban reformation around freeways and incorporating urban design principles prioritizing social equity, environmental sustainability, and economic revitalization for the most vulnerable and impacted communities. The proposed designs aim to restore community cohesion and foster resilient urban ecosystems.This study contributes to the ongoing discourse on sustainable urban transformation and the reclamation of public spaces for community benefit by providing a comprehensive analysis of historical impacts and contemporary solutions. The paper's findings should help inform and guide urban planning and policy advocates and professionals inspired by the reconnecting communities movement, underscoring the need for innovative approaches to mending bifurcated and ecologically disadvantaged communities.

Wildfires are increasing in duration and intensity across the United States' Pacific West region, resulting in heightened particulate matter from smoke in the atmosphere. Levels of peak particulate matter are concurrent to peak visitor attendance at National Parks, given seasonal alignment with summer vacation travel and heightened forest fire conditions. Particulate matter threatens visitor health and safety and contributes to poor visibility and a deteriorated visitor experience. To assess visitation response to diminished air quality, we utilized wildfire-generated particulate matter (PM2.5) data in conjunction with monthly attendance records for three ecoregions containing eight national parks in Washington, Oregon, and California from 2009 to 2019. We analyzed daily PM2.5 levels from data gridded at the 10 km scale for National Park Service units by Level III forest ecoregions within the National Park Service's Pacific West Unit. Data were then compared to normalized monthly visitation trends for each of the ecoregions using two statistical methods Kendall's Tau and Analysis of Variance (ANOVA) with post-hoc Tukey tests. Results demonstrate that attendance at these national parks does not decrease in response to increased PM2.5 levels. Instead, we see several statistically significant increases in attendance across these ecoregions during periods of reduced air quality. Of 115 shifts between air quality categories during the busy season of July to September, there are no significant decreases in attendance as air quality worsens. These findings suggest that visitors are willing to tolerate reduced air quality compared to other factors such as temperature or precipitation. Given that park units within each ecoregion feature diverse historical contexts, varied built environments, and unique ecological systems, our discussion specifically addresses managerial concerns associated with maintained high levels of visitation during suboptimal, and potentially dangerous, conditions. There is substantial need for specific, scalable approaches to mitigate adverse health and experiential impacts as visitors are exposed to increased risks during a range of exertional activities associated with diverse settings.

Wildfires are a significant threat to human health, in part through degraded air quality. Prescribed burning can reduce wildfire severity but can also lead to an increase in air pollution. The complexities of fires and atmospheric processes lead to uncertainties when predicting the air quality impacts of fire and make it difficult to fully assess the costs and benefits of an expansion of prescribed fire. By modeling differences in emissions, surface conditions, and meteorology between wildfire and prescribed burns, we present a novel comparison of the air quality impacts of these fire types under specific scenarios. One wildfire and two prescribed burn scenarios were considered, with one prescribed burn scenario optimized for potential smoke exposure. We found that PM2.5 emissions were reduced by 52%, from 0.27 to 0.14 Tg, when fires burned under prescribed burn conditions, considerably reducing PM2.5 concentrations. Excess short-term mortality from PM2.5 exposure was 40 deaths for fires under wildfire conditions and 39 and 15 deaths for fires under the default and optimized prescribed burn scenarios, respectively. Our findings suggest prescribed burns, particularly when planned during conditions that minimize smoke exposure, could be a net benefit for the impacts of wildfires on air quality and health.

To demonstrate how a mega city can lead in decarbonizing beyond legal mandates, the city of Los Angeles (LA) developed science-based, feasible pathways towards utilizing 100% renewable energy for its municipally-owned electric utility. Aside from decarbonization, renewable energy adoption can lead to co-benefits such as improving urban air quality from reductions in combustion-related emissions of oxides of nitrogen (NOx), primary fine particulate matter (PM2.5) and others. Herein, we quantify changes to air pollutant concentrations and public health from scenarios of 100% renewable electricity adoption in LA in 2045, alongside aggressive electrification of end-use sectors. Our analysis suggests that while ensuring reliable electricity supply, reductions in emissions of air pollutants associated with the 100% renewable electricity scenarios can lead to 8% citywide reductions of PM2.5 concentration while increasing ozone concentration by 5% relative to a 2012 baseline year, given identical meteorology conditions. The combination of these concentration changes could result in net monetized public health benefits (driven by avoided deaths) of up to $1.4 billion in year 2045 in LA, results potentially replicable for other city-scale decarbonization scenarios.

Observations of near‐surface NO2 show a diurnal pattern with midday minima and daily maxima in the morning and evening. These surface cycles are dependent on chemical processing, transport, and emissions. We evaluate these cycles with the United States Environmental Protection Agency (EPA) community multiscale air quality (CMAQ) model data from the EPA Air Quality Time Series (EQUATES) project, compared with ground‐based measurements from the EPA air quality system, two Pandora measurement sites, and satellite data from TROPOMI. We find that the morning vertical column density (VCD) lags surface concentrations by 1 hr on average, where this lag varies with location and day. The peak VCD can also lead the surface maximum concentration, especially in the evening, responding to transport and afternoon compression of the boundary layer. Modeled NO2 VCD is sensitive to column calculation technique. With hourly daytime satellite‐based NO2 observations newly available from the TEMPO instrument, the timing and magnitude of cycles in near‐surface NO2 versus column NO2 will help inform the utilization of hourly satellite data. This work will help inform the timing of surface‐column connections to better interpret new hourly satellite observations for health and air quality applications, including emissions characterization. Plain Language Summary: Nitrogen dioxide (NO2) is a major air pollutant that may be measured from the ground or from satellites in space. Over the U.S., hourly satellite data are now available from a new instrument called TEMPO. We use a computer model to understand the expected relationship between the satellite column and ground‐level measurements. We find that the connection between the surface and column varies across the day: TEMPO observations are more likely to see morning peak and daytime minimum near‐surface amounts; TEMPO observations may miss evening peak near‐surface amounts. By helping to connect space‐based data with on the ground air quality, this work will help inform the timing of surface‐column connections to better interpret new hourly satellite observations for health and air quality applications. Key Points: Satellite observations of nitrogen dioxide (NO2) show column values, which show different hourly patterns than surface monitoring of NO2Column NO2 values lag surface NO2 in the morning but lead surface NO2 in the eveningModel simulations of column NO2 are sensitive to calculation approach [ABSTRACT FROM AUTHOR]

The Southeast Asian Region is one of the world's regions that has historically had poor air quality conditions due to excess fossil fuel usage, coal combustion, and intensive deforestation activities. As the hub of economic activity, Indonesia's Jakarta and Palembang cities have drawn significant attention from rural areas, leading to unrestrained urbanization in the past decade. Therefore, this study aimed to integrate the two urban centers of Indonesia, Jakarta and Palembang. It analyzed the variation of nitrogen dioxide (NO 2) concentration, vegetation cover, and land surface temperature (LST) across all seasons from 2021 to 2023. We retrieved the tropospheric NO 2 concentration data from the ozone monitoring instrument (OMI) aboard Aura Satellite, and obtained LST and the normalized difference vegetation index (NDVI) from Landsat 8 OLI images. We then used a Pearson correlation analysis to determine the relationship between the NO 2 concentration, NDVI, and LST. The findings showed that Jakarta had higher levels of air pollution than Palembang, with rising LST and NO 2 levels. During the study period, the mean NO 2 concentration in Jakarta and Palembang was 3.25 × 1015 mol/cm2 and 0.87 × 1015 mol/cm2, respectively. While, the mean LST values in Jakarta and Palembang were 33.97 °C and 27.17 °C, respectively. The correlation results indicated a negative relationship between NO 2 level and NDVI (r = −0.378, p < 0.01), while it obtained a positive association with LST (r = 0.421, p < 0.01). These results can be a chance for the authority to formulate a new strategy for mitigating air pollution, especially for NO 2 pollutants in urban areas. [ABSTRACT FROM AUTHOR]

In indoor environments, air quality significantly impacts human health and well-being, with carbon monoxide (CO) posing a particular hazard due to its colorless and odorless nature and potential to cause severe health issues. Integrating the Internet of Things and remote sensing technologies has revolutionized data monitoring, collection, and evaluation, especially within the context of 'smart' homes. This study leverages these technologies to enhance indoor air quality monitoring. By collecting data on key indoor atmospheric quality indicators—carbon dioxide (CO2), methane (CH4), alcohol, liquefied petroleum gas (LPG), particulate matter (PM1 and PM2.5), humidity, and temperature—the study aims to predict indoor carbon monoxide levels. A custom dataset was compiled from August to October, consisting of 61,710 observations recorded at one-minute intervals. The methodology employs a stacking ensemble approach, integrating multiple machine learning models to boost prediction accuracy and reliability. In the stacking ensemble, six distinct models are employed: Random Forest, Multi-Layer Perceptron, Lasso, Elastic Net, XGBoost, and Support Vector Regression. Each model is individually trained and fine-tuned using the Grid Search method to optimize parameter combinations. These optimized models are then combined in the stacking ensemble, which achieves a Mean Squared Error (MSE) of 0.0140, a Root Mean Squared Error (RMSE) of 0.1185, and a Mean Absolute Error (MAE) of 0.0291. The results demonstrate that the proposed system significantly enhances the precision of CO prediction, underscoring its critical role in air quality surveillance within smart environments. [ABSTRACT FROM AUTHOR]

The TROPOspheric Monitoring Instrument (TROPOMI), onboard the Sentinel-5 Precursor (S5P) satellite launched in October 2017, is dedicated to monitoring the atmospheric composition associated with air quality and climate change. This paper presents the global retrieval of TROPOMI tropospheric formaldehyde (HCHO) and nitrogen dioxide (NO2)vertical columns using an updated version of the Peking University OMI NO2 (POMINO) algorithm, which focuses on improving the calculation of air mass factors (AMFs). The algorithm features explicit corrections for the surface reflectance anisotropy and aerosol optical effects, and uses daily high-resolution (0.25°×0.25°) a priori HCHO and NO2 profiles from the Global Earth Observing System Composition Forecast (GEOS-CF) dataset. For cloud correction, a consistent approach is used for both HCHO and NO2 retrievals, where (1) the cloud fraction is re-calculated at 440 nm using the same ancillary parameters as those used in the NO2 AMF calculation, and (2) the cloud top pressure is taken from the operational FRESCO-S cloud product. The comparison between POMINO and reprocessed (RPRO) operational products in April, July, October 2021 and January 2022 exhibits high spatial agreement, but RPRO tropospheric HCHO and NO2 columns are lower by 10 % to 20 % over polluted regions. Sensitivity tests with POMINO show that the HCHO retrieval differences are mainly caused by different aerosol correction methods (implicit versus explicit), prior information of vertical profile shapes and background corrections; while the NO2 retrieval discrepancies result from different aerosol corrections, surface reflectances and a priori vertical profile shapes as well as their non-linear interactions. With explicit aerosol corrections, the HCHO structural uncertainty due to the cloud correction using different cloud parameters is within ± 20 %, mainly caused by cloud height differences. Validation against ground-based measurements from global Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) observations and the Pandonia Global Network (PGN) shows that in April, July, October 2021 and January 2022, POMINO retrievals present a comparable day-to-day correlation but a reduced bias compared to the RPRO products (HCHO: R = 0.62, NMB = −30.8 % versus R = 0.68, NMB = −35.0 %; NO2: R = 0.84, NMB = −9.5 % versus R = 0.85, NMB = −19.4 %). An improved agreement of HCHO/NO2 ratio (FNR) with PGN measurements based on POMINO retrievals is also found (R = 0.83, NMB = −18.4 % versus R = 0.82, NMB = −24.1 %). Our POMINO retrieval provides a useful source of information particularly for studies combining HCHO and NO2. [ABSTRACT FROM AUTHOR]

Indoor air quality (IAQ) is crucial to environmental health significantly impacting on the well-being and productivity of building occupants. Several studies have explored various aspects of IAQ in non-tropical regions but there is limited information on how seasonal variations affect IAQ in hot-humid climates like Enugu, Nigeria. This study investigated seasonal changes in key indoor air parameters including CO, CO2, HCHO, TVOC, temperature, RH, PM2.5, and AQI across rainy and dry seasons in 58 government offices in Enugu, and evaluated how these fluctuations relate to architectural features of the offices. Using a mixed-methods approach, data collection involved qualitative assessments of building design attributes alongside quantitative IAQ measurements taken with the BOSEAN T-Z01Pro detector. Seasonal variations were analyzed using paired T-tests, ANOVA, and regression models. The results revealed a marked increase in pollutant concentrations during the dry season (p < 0.001–0.005), resulting in a poorer air quality index compared to the rainy season. Architectural features accounted for 68.5% of the variability in AQI (R2 = 0.685, p = 0.000), with casement windows being significantly associated with better air quality (Exp B = -4.217, p = 0.013) These shows that the dry season poses a greater risk to IAQ which is worsened in offices where projecting windows were used, potentially impacting health and productivity. The study emphasizes the need to address seasonal IAQ differences when designing office buildings in tropical regions. Design architects can help reduce seasonal air quality challenges and support healthier, more productive indoor environments by incorporating ventilation strategies, such as the use of casement windows. Further research should include long-term monitoring across various settings and additional IAQ parameters for better understanding of IAQ dynamics. [ABSTRACT FROM AUTHOR]

Particulate matter (PM) emissions from anthropogenic sources contribute substantially to air pollution. The unequal adverse health effects caused by source-emitted PM emphasize the need to consider the discrepancy of PM-bound chemicals rather than solely focusing on the mass concentration of PM when making air pollution control strategies. Here, we present a dataset about chemical compositions of real-world PM emissions from typical anthropogenic sources in China, including industrial (power, industrial boiler, iron & steel, cement, and other industrial process), residential (coal/biomass burning, and cooking), and transportation sectors (on-road vehicle, ship, and non-exhaust emission). The data was obtained under the same strict quality control condition on field measurements and chemical analysis, minimizing the uncertainty caused by different study approaches. The concentrations of PM-bound chemical components, including toxic elements and PAHs, exhibit substantial discrepancies among different emission sectors. This dataset provides experimental data with informative inputs to emission inventories, air quality simulation models, and health risk estimation. The obtained results can gain insight into understanding on source-specific PMs and tailoring effective control strategies. [ABSTRACT FROM AUTHOR]

The moss bag technique was applied on Kamchatka to assess the effect of Shiveluch volcano on the air quality. Moss bags were exposed in settlements located at different distances from the volcano and affected by ashfall to varying degrees. The inductively coupled plasma optical emission spectrometry was used for the determination of the content of Al, Ba, Co, Cd, Cr, Cu, Fe, Mn, P, Pb, Sr, S, V, and Zn in both exposed and unexposed moss samples. Relative accumulation factor revealed enrichment of mosses exposed in the area affected by ashfall with Al, Cr, V, and Zn. Correlation and principal component analysis allowed to identify elements of volcanogenic, geogenic, and anthropogenic origin. Ecological risk values less than 150 indicate low potential ecological risk in the region. Active moss biomonitoring can be considered a cheap and efficient tool for in-depth study of the influence of volcanic activity on air quality on the peninsula. [ABSTRACT FROM AUTHOR]

ABSTRACT Particulate matter (PM) is one of the most important air pollutants that can hurt human health. Fireworks are rare but important sources of ambient PM and can therefore lead to short‐term exposure to high concentrations of PM with potentially toxic components. For the first time in the city of Rijeka, Croatia, the characteristics of fireworks PM during 6 celebrations (New Year 2015, 2017, and 2018 and 3 football events in May 2017) were investigated and compared with the average concentrations during the surrounding period. The elemental composition of the PM samples was determined using the X‐ray fluorescence technique, where the concentrations of 15 elements from S to Pb were determined. Although the total PM concentrations were in the range of values previously measured at the same locations, the concentrations of several elements were substantially larger during the fireworks. A significant increase in concentrations was observed for K, Ti, Cr, Cu, Sr, and Ba, with the largest increase in concentration for Sr and Ba. The most noticeable increase in concentrations compared to the seasonal average was observed during the 2018 New Year's sampling when the average 8‐h concentrations were elevated as follows: 11 times for K, 14 times for Ti, 5 times for Cr, 9 times for Cu, 691 times for Sr, and 243 times for Ba. The increase in concentrations at other events where 24‐h samples were taken was present but not as pronounced and was lowest at football celebration events. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TC) are dynamic atmospheric phenomena featuring extreme low-pressure systems and powerful winds, known for their devastating impacts on weather and the environment. The main purpose of this paper is to consider the subtle involvement of TCs in the air quality index (AQI), focusing on aspects related to the air quality before, during and after cyclones. This research employs multimodal methods, which include meteorological data and different satellite observations. Deep learning approaches, i.e., ConvLSTM, CNN and Real-ESRGAN models, are combined with a regression model to analyze the temporal variability in the air quality associated with tropical cyclones. Deep learning models are deployed to uncover complex patterns and non-linear interdependencies between cyclones' features and the AQI to give predictive insights into the air quality fluctuations throughout the different stages of tropical cyclones. Furthermore, this study explores the aftermaths of TCs in terms of the air quality with respect to post-cyclone recovery. The findings offer an enhanced view of the role of TCs in the regional or global air quality, which will be useful for policymakers, meteorologists and environmental researchers. Utilizing a CNN for tropical cyclone (TC) classification and the extra trees regressor (ETR) for AQI prediction results in accuracy of 92.02% for the CNN and an R 2 of 83.33% for the ETR. Hence, this work adds to our knowledge and enlightens us on the complex interactions between TCs and the air quality, highlighting wider public health concerns regarding climate adaptation and urban renewal. [ABSTRACT FROM AUTHOR]

Background and Objective: The updated World Health Organization (WHO) air quality guideline recommends an annual mean concentration of fine particulate matter (PM2.5) not exceeding 5 or 15 μg/m3 in the short‐term (24 h) for no more than 3–4 days annually. However, more than 90% of the global population is currently exposed to daily concentrations surpassing these limits, especially during extreme weather conditions and due to transboundary dust transport influenced by climate change. Herein, the effect of respirable

According to the World Health Organization (WHO, Ambient (outdoor) air pollution (who), 2020), outdoor air pollution is estimated to have caused 4.2 million premature deaths worldwide in 2019. Under this scenario, the development of statistical tools for assessing the impact of a possible reduction of fossil fuels-based transportation systems on air quality is more relevant than ever. This type of instruments can help policy makers to take the right path to tackle the problem of air pollution in urban environments. Thus, this work proposes a method to evaluate the impact of the reduction of vehicle fleets on air quality. The method is based on the construction of counterfactual time series, which represent observed air pollutants under a treatment of interest. In this case, the treatment of interest are the vehicle fleets on the streets. The construction of the counterfactual time series is based on Bayesian dynamic linear models. Several impact assessment measures are implemented, taking advantage of the counterfactual and observed time series (the observed pollutant without the treatment). The flexibility of the Bayesian approach allows easy inference of the impact quantities via high posterior density intervals. Some simulation analyses show that the method works well under different impact scenarios, and one application regarding Medellin's case in Colombia is presented. For the application, it is shown that the drastic reduction in vehicle fleets during the COVID-19 mobility restrictions led to a significant reduction in the presence of different pollutants ( PM 10 , PM 2.5 , NO 2 , NO x ) [ABSTRACT FROM AUTHOR]

The paper aims at investigating the effectiveness of estimating vertical profiles of air temperature and PM10 concentrations in Alpine valleys through ground stations positioned at different altitudes on one valley sidewall (i.e., pseudovertical profiles). Two case studies in the Italian Alps are investigated: Chiese Valley in Trentino Province and Camonica Valley in the Lombardy region. Vertical profiles of temperature and PM10 concentrations were derived from airborne measurements at the center of the two valleys by means of low-cost sensors installed on a drone during summer 2019 and a tethered balloon during winter 2020. At the same time, five stations, equipped with the same kind of low-cost sensors, simultaneously monitored the same variables on one mountain slope. Comparisons between pseudoprofiles and airborne soundings revealed that ground stations well approximated temperature and PM10 soundings during the night and early morning, while temperatures along the slopes were higher than in the center of the valley during daytime, due to solar radiative heating, with larger differences in summer than in winter. On the contrary, some episodes with PM10 concentrations slightly higher in the valley center than on the slope were recorded, due to transport events and upslope winds. Nonetheless, the pseudoprofiles based on slope ground measurements faithfully reproduced the vertical gradients of both air temperature and PM10 if compared to those assessed from the soundings performed at the center of the two valleys. Results show that pseudovertical profiles can be a reliable and inexpensive method for continuous monitoring of vertical air temperature and PM10 distribution in mountain valleys. Significance Statement: The purpose of this study is to understand whether ground-based measurements of air temperature and particulate matter on mountain slopes can be used as suitable surrogates of vertical atmospheric soundings with tethered balloons or drones in mountain environment. This is important because balloon and drone soundings are expensive and regulative constrained. The knowledge of the vertical distribution of air temperature is crucial to predict the distribution of air pollutants in valleys, namely, the particulate matter emitted by wood burning. Our results showed that ground-based measurements on mountain slopes made with low-cost sensors can satisfactorily reproduce vertical gradients of air temperature and distribution of particulate matter in a reliable and inexpensive way. [ABSTRACT FROM AUTHOR]

A major data gap in tropical river ecology is the limited documentation of natural water purification of upstream rivers. This study documented the improvement of water quality of downstream Balili River as affected by environmental factors and macrophyte diversity using a mixed-method approach (water quality assessment, plant inventory, field observation, canonical correspondence analysis, community interview). Results showed that the distance from pollution plays a significant role in the self-purification of the river while the floral diversity maintains the riparian from further contaminating the water and at the same time absorbing air pollutants. Generally, the pollution reduction and %change is exponential at 2 and 3 km from point source then taper at 4 and 5 km. At 3 km from the point source, 30% improvement in TDS, BOD and salinity, 25% for EC, 20% in TSS, 35% in WQI, 36% in CPI, and 50% for DO were noted. Key factors that influence the self-purification process in the river were distance from pollution source, elevation, human disturbance (as indicated by %tree canopy) and temperature. These findings highlight the importance of distance from pollution sources and floral diversity in driving water quality improvements, with implications for sustainable water resource management in tropical regions. NOVELTY STATEMENT: This study documented the improvement of water quality of downstream Balili River as affected by environmental factors and macrophyte diversity – as contribution to the major data gap in tropical river ecology which currently has limited documentation on natural water purification of upstream rivers. The study also shows the mosaic interplay of water quality, plant diversity, distance from pollution, human disturbance, and air quality in understudied tropical river. Further, this study is unique in documenting the downstream of Balili River since majority of the previous studies were concentrated in the upstream portion of the river. [ABSTRACT FROM AUTHOR]

The application of innovative systems using low-cost microcontrollers in human biometeorology studies is a promising alternative to conventional monitoring devices, which are usually cost-intensive and provide measurements at specific points, as in stationary meteorological stations. A Portable Low-cost Environmental Monitoring System (PLEMS) aimed at the pedestrian scale is introduced. The backpack-type equipment consists of a microcontroller with attached sensors that assess environmental conditions in a broad sense, integrating measurements of air quality, lighting and noise levels alongside variables typically measured at meteorological stations. The application of the system took place in altogether 12 environmental walks carried out with questionnaire-surveys with concurrent environmental monitoring with the PLEMS in Curitiba, Brazil, a subtropical location characterized by a Cfb climate type. Results allowed us to test the equipment and method of data gathering within a limited period (approximately 50 min) and for a short walking circuit of 800 m. The equipment was successfully able to capture even slightest differences in environmental conditions among points of interest, whereas subjective responses (n= 3843 responses to a total of 11 questions) showed consistency with measured data. From a multi-domain perspective, relevant insights could be obtained for the measured variables. [ABSTRACT FROM AUTHOR]

The shifting frontiers of air pollution emission sources contribute to stagnation or reversal of air quality gains across the United States (US). The frequency and possible duration of Exceptional Events – driven primarily by wildfires and dust storms – have significantly increased in the US over the past decade. Combined with the US Environmental Protection Agency (EPA) final rule strengthening primary annual National Ambient Air Quality Standards (NAAQS) for PM2.5 by 25%, communities will need to reevaluate domestic and international sources of PM2.5. This study applies the Isolation Forest methodology to Exceptional Event demonstrations to flag and evaluate sources of anomalies in large PM2.5 measurement datasets. Focusing on a decade of hourly PM2.5 data measured in seven regions across Texas from 2012 to 2021 (>3 million data points), we present methods to efficiently flag hourly PM2.5 anomalies with compute times of ~minutes and characterize their spatial impacts as local or (multi-) regional; subsequent evaluation of potential sources of the increase can then be conducted more efficiently in a targeted manner. For a subset of anomalies, we incorporate air mass back trajectories, surface influences, and positive matrix factorization to evaluate potential sources. Our anomaly characterization method separated statistically normal PM2.5 data and enabled differentiation of localized versus larger-scale PM2.5 sources. In addition, our method successfully characterized the Summer 2020 severe Saharan dust intrusions into Texas, as well as the influence of international smoke from Mexico on El Paso's regional air quality. This anomaly flagging and characterization method is promising for assessing the relative importance of sources to anomalies in PM2.5 and other criteria air pollutants for multiple purposes; while this work focuses on its capacity for exceptional event demonstrations, the applicability includes long-term trend analyses from environmental justice analyses of air pollutant exposure to air quality attainment demonstrations. Implications: The shifting frontiers of air pollution emission sources contribute to stagnation or reversal of air quality gains across the United States (US). The frequency and possible duration of Exceptional Events – driven primarily by wildfires and dust storms – have significantly increased in the US over the past decade. Combined with the US Environmental Protection Agency (EPA) final rule strengthening primary annual National Ambient Air Quality Standards (NAAQS) for PM2.5 by 25%, communities will need to reevaluate domestic and international sources of PM2.5. This study presents a robust methodology to rapidly flag and evaluate sources of anomalies in PM2.5 measurements. This anomaly flagging and characterization method is promising for assessing the relative importance of sources to anomalies in PM2.5 and other criteria air pollutants for multiple purposes; while this work focuses on its capacity for exceptional event demonstrations, the applicability includes long-term trend analyses from environmental justice analyses of air pollutant exposure to air quality attainment demonstrations. [ABSTRACT FROM AUTHOR]

Using a high-resolution, 1.33 km by 1.33 km coupled Weather Research and Forecasting-Community Multi-scale Air Quality Model (WRF-CMAQ), we quantify the impact of emissions of nitrogen oxides (NOx) from high energy demand day (HEDD) electricity generating units (EGU) and onroad vehicles on ambient ozone air quality in the Long Island Sound Tropospheric Ozone Study (LISTOS) region covering New York City (NYC); Long Island, NY; coastal Connecticut; and neighboring areas. We test sensitivity scenarios to quantify HEDD EGU NOx contributions to ozone: (1) zero out HEDD EGU emissions, (2) dispatch HEDD EGUs starting with the lowest NOx emitting units first, (3) reduce onroad emissions by 90%, (4) combine zero out HEDD EGU emissions and reducing onroad emissions by 90%, and (5) dispatch HEDD EGUs starting with the lowest emitting units coupled with a reduction in onroad emissions by 90%. Results determine that HEDD EGUs lead to highly localized impacts on ambient concentrations of ozone while onroad emission reductions lead to large-scale regional concentration impacts. Further, reducing onroad emissions by 90% leads to spatially smaller VOC-limited regions and spatially larger transitional and NOX-limited regions around NYC. Despite the limited scale at which the EGU emission reductions occur, modifying HEDD EGU NOX emissions still provides substantial benefits in reducing ozone concentrations in the region, particularly at elevated ozone concentrations above 70 ppb. Implications: High-resolution coupled meteorology-chemistry modeling was used to quantify the impacts of high energy demand day (HEDD) electricity generating units (EGUs) and onroad transportation emissions changes on ozone air quality in the LISTOS region. Despite being highly localized and variable, HEDD EGUs NOX emissions sensitivity tests led to quantifiable changes in ozone. Further, reducing onroad emissions by 90% produced large decreases in ozone concentrations and led to a more NOX-sensitive ozone photochemical regime. With a transition to greater NOX-sensitivity, urban NOX-titration weakens and ozone is more likely to decline with the removal of additional NOX from sources like HEDD EGUs. [ABSTRACT FROM AUTHOR]

A real-time air quality forecasting system was developed using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to provide support for flight planning activities during the NOAA Atmospheric Emissions and Reactions Observed from Megacities to Marine Areas (AEROMMA) and NASA Synergistic TEMPO Air Quality Science (STAQS) 2023 field campaigns. The forecasting system operated on two separate domains centered on Chicago, IL, and New York City, NY, and provided 72-hour predictions of atmospheric composition, aerosols, and clouds. This study evaluates the Chicago-centered forecasting system's 1-, 2-, and 3-day ozone (O3) forecast skill for Chiwaukee Prairie, WI, a rural area downwind of Chicago that often experiences high levels of O3 pollution. Comparisons to vertical O3 profiles collected by a Tropospheric Ozone Lidar Network (TOLNet) instrument revealed that forecast skill decreases as forecast lead time increases. When compared to surface measurements, the forecasting system tended to underestimate O3 concentrations on high O3 days and overestimate on low O3 days at Chiwaukee Prairie regardless of forecast lead time. Using July 25, 2023, as a case study, analyses show that the forecasts underestimated peak O3 levels at Chiwaukee Prairie during this regionwide bad air quality day. Wind speed and direction data indicates that this underestimation can partially be attributed to lake breeze simulation errors. Surface fine particulate matter (PM2.5) measurements, Geostationary Operational Environmental Satellite-16 (GOES-16) aerosol optical depth (AOD) data, and back trajectories from the NOAA Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model show that transported Canadian wildfire smoke impacted the Lake Michigan region on this day. Errors in the forecasted chemical composition and transport of the smoke plumes also contributed to underpredictions of O3 levels at Chiwaukee Prairie on July 25, 2023. The results of this work help identify improvements that can be made for future iterations of the WRF-Chem forecasting system. Implications: Air quality forecasting is an important tool that can be used to inform the public about upcoming high pollution days so that individuals may plan accordingly to limit their exposure to health-damaging air pollutants. Forecasting also helps scientists make decisions about where to make observations during air quality field campaigns. A variety of observational datasets were used to evaluate the accuracy of an air quality forecasting system that was developed for NOAA and NASA field campaigns that occurred in the summer of 2023. These evaluations inform areas of improvement for future development of this air quality forecasting system. [ABSTRACT FROM AUTHOR]

This study used mixed methods to explore the local peoples' perceptions of the ecological services provided by the Lake Bosomtwe. Targeting household heads, traditional leaders, and assembly members, it uses simple random sampling to survey 309 households and conducts 14 focus group discussions in seven communities. Findings indicate a good local understanding of the lake's ecological services, with provisioning services, particularly freshwater and fish, being highly valued. The study emphasizes the significance of air quality control within regulatory services. The study concluded that the lake provides vital ecological services that sustain the livelihoods of the local community. [ABSTRACT FROM AUTHOR]

The Permian Basin is the largest petroleum producing region in the United States. Few studies have investigated impact the Petroleum Industry had on the air quality of the Permian Basin. Industry operations in the Basin has two primary impacts on air quality, (a) it significantly contributes carbon dioxide (CO2) and methane (CH4) to the atmosphere during extraction, transport and use of fossil fuels, (b) industry operations influence public health especially when populations are in proximity to all categories of oil and gas facilities. The advent of Covid-19 in late 2019 and a severe drought that began prior to 2019 in West Texas have created unusual circumstances regarding air quality and public health in the Permian Basin. This study has monitored airborne pollen, spores, fungal spores, and other organically derived particles, and inorganic particulate matter (1, 2.5, 10-micron particulate matter (PM) and total suspended particles (TSP)), volatile organic compounds (VOCs), and sulfur dioxide (SO2). The pandemic caused a historic reduction in oil and gas production slowing operations in the Permian Basin to near zero during the study period, reducing environmental effects on air quality and concomitantly an increase in flaring of natural gas. Although there were increases in the release of VOCs fixed base monitors detected no notable increase. However, an increase in the concentration of airborne pollutants (VOCs and SO2) shows a close correlation with proximity to oil and gas facilities and the intensity of industry operations. This phenomenon is attributed to the highly local nature of flaring and CH4 plume emissions from leaking oil and gas facilities. Air quality improved in the Permian Basin due to reduction in production activity because of the Covid-19 pandemic, and the reduction of particulate matter due to the ongoing drought during the study period. [ABSTRACT FROM AUTHOR]

Foliar fungi on urban trees are important for tree health, biodiversity and ecosystem functioning. Yet, we lack insights into how urbanization influences foliar fungal communities. We created detailed maps of Stockholm region's climate and air quality and characterized foliar fungi from mature oaks (Quercus robur) across climatic, air quality and local habitat gradients. Fungal richness was higher in locations with high growing season relative humidity, and fungal community composition was structured by growing season maximum temperature, NO2 concentration and leaf litter cover. The relative abundance of mycoparasites and endophytes increased with temperature. The relative abundance of pathogens was lowest with high concentrations of NO2 and particulate matter (PM2.5), while saprotrophs increased with leaf litter cover. Our findings show that urbanization influences foliar fungi, providing insights for developing management guidelines to promote tree health, prevent disease outbreaks and maintain biodiversity within urban landscapes. [ABSTRACT FROM AUTHOR]

Numerous studies rely on long-term PM2.5 speciation data from the EPA's Chemical Speciation Network (CSN), for example, to estimate health impacts or investigate the sources and transport of PM2.5 pollution. These studies rely on consistent, long-term time series measurements of PM2.5 species to draw conclusions about PM2.5 emissions sources and their health impacts. However, changes in contractors and associated methodological changes in 2015 and 2018 led to disruptions in the consistency of the CSN data, specifically, concentration discontinuities in the CSN time series for ions and elemental carbon (EC) from November 2015 to September 2018 and from October 2018 onward, respectively. To address the impact of these changes on downstream air quality and health analyses, this study developed correction factors by comparing collocated CSN measurements to measurements from the Interagency Monitoring of Protected Visual Environment (IMPROVE) network, which used consistent instrumentation and contractors throughout the study period. These correction factors reduced the discontinuities in the ions and EC concentration time series data, which could be critical for time series source apportionment receptor modeling, air pollution policy and accountability investigations, and health effect studies. Copyright © 2024 American Association for Aerosol Research [ABSTRACT FROM AUTHOR]

Linking meteorology and air pollutants is a key challenge. The study investigated meteorological effects on PM 2.5 concentration using the advanced convergent cross mapping method, utilizing hourly PM 2.5 concentration and six meteorological factors across eight provinces and cities in Vietnam. Results demonstrated that temperature (ρ = 0.30) and radiation (ρ = 0.30) produced the highest effects, followed by humidity (ρ = 0.28) and wind speed (ρ = 0.24), while pressure (ρ = 0.22) and wind direction (ρ = 0.17) produced the weakest effects on PM 2.5 concentration. Comparing the ρ values showed that temperature, wind speed, and wind direction had greater impacts on PM 2.5 concentration during the dry season whereas radiation had a more influence during the wet season; Southern stations experienced larger meteorological effects. Temperature, humidity, pressure, and wind direction had both positive and negative influences on PM 2.5 concentration, while radiation and wind speed mostly had negative influences. During PM 2.5 pollution episodes, there was more contribution of meteorological effects on PM 2.5 concentration indicated by ρ values. At contaminated levels, humidity (ρ = 0.45) was the most dominant factor affecting PM 2.5 concentration, followed by temperature (ρ = 0.41) and radiation (ρ = 0.40). Pollution episodes were pointed out to be more prevalent under higher humidity, higher pressure, lower temperature, lower radiation, and lower wind speed. The ρ calculation also revealed that lower temperature, lower radiation, and higher humidity greatly accelerated each other under pollution episodes, further enhancing PM 2.5 concentration. The findings contributed to the literature on meteorology and air pollution interaction. [ABSTRACT FROM AUTHOR]

To mitigate the societal impact of the COVID-19 pandemic, China implemented long-term restrictive measures. The sudden liberalization at the end of 2022 disrupted residents' daily routines, making it scientifically intriguing to explore its effect on air quality. Taking Chongqing City in Southwest China as an example, we examined the impact of restriction liberalization on air quality, identified potential sources of pollutants, simulated the effects of abrupt anthropogenic control relaxation using a Random Forest Model, and applied an optimized model to predict the post-liberalization pollutant concentrations. The results showed increases in PM2.5 (72.3%), PM10 (67.7%), and NO2 (21.9%) concentrations, while O3 concentration decreased by 20.5%. Although potential pollution source areas contracted, pollution levels intensified with northeastern Sichuan, interior Chongqing, and northern Guizhou being major contributors to pollutant emissions. Anthropogenic emissions accounted for 26.7 ~ 33% changes in PM2.5 and PM10 concentrations while meteorological conditions contributed to 40.2 ~ 43.3% variations observed during the period. The optimized model demonstrated a correlation between predicted and observed values with R2 ranging from 0.70 to 0.89, enabling accurate prediction of post-liberalization pollutant concentrations. This study can enhance our understanding regarding the impact of sudden social lockdown relaxation events on air quality while providing support for urban air pollution prevention. [ABSTRACT FROM AUTHOR]

The increasing demand for vehicles is leading to a rise in pollutant emissions across the world. This decline in air quality is significantly impacting public health, with internal combustion engines being a major contributor to this concerning trend. Ever-stringent regulations demand high engine efficiency and reduced pollutant emissions. Therefore, every automobile company requires rigorous methods for accurately estimating engine emissions. The implementation of advanced technologies, including machine learning methods, has proven to be a promising solution. The present work aims to develop an artificial intelligence-based model to estimate the pollutant emissions produced by an internal combustion engine under varying operating conditions. Experimental activities have been conducted on a single-cylinder spark ignition research engine with gasoline port fuel injection under both stationary and dynamic operating conditions. This work explores different artificial intelligence architectures and compares their performance in order to determine the best approach for the presented task. These structures have been trained and tested based on data obtained from the engine control unit and fast emission analyzer. The main target is to evaluate the possibility of applying the presented artificial intelligence predictive model as an on-board virtual tool in the estimation of emissions in real driving conditions. [ABSTRACT FROM AUTHOR]

This paper examines the impact of renewable energy heating on air quality in China, using the Qinling Mountains–Huaihe River line as a quasi-natural experiment to distinguish between regions with central heating and those without. Employing a difference-in-differences approach and analyzing panel data from 298 cities between 2014 and 2022, our findings indicate that the renewable energy heating policy has significantly improved air quality. Specifically, the policy led to substantial improvements in air quality, reducing concentrations of key pollutants: SO2 by 28.31%, CO by 7.57%, NO2 by 5.72%, and PM2.5 by 7.15%. The policy's effects are most pronounced in regions with lower temperatures and in the eastern parts of the country. Further analysis emphasizes the critical role of energy transition, environmental regulations, and government investment in technology as key drivers of these air quality improvements. [ABSTRACT FROM AUTHOR]

Plants play a vital role in mitigating aerosol particles and improving air quality. This study investigated the composition characteristics and potential effects of particles retained on the leaf surfaces of two amphibious plants (i.e., Alternanthera philoxeroides and Hydrocotyle vulgaris) in both terrestrial and aquatic habitats. The results show that plant habitats influenced the composition of aerosol particles retained on leaf surfaces. Specifically, plants in terrestrial habitats retained a higher mass concentration of coarse and large particles rich in inorganic Ca2+, accounting for over 70% of total ions, whereas plants in aquatic habitats retained a greater abundance of fine and secondary particles with high fractions of water-soluble NO3− and SO42−, taking up over 65% of total anions. Secondary particles deposited on the surfaces of plants in aquatic habitats tend to deliquesce and transform from the particle phase to the liquid phase. Terrestrial habitats facilitate the deposition of large particles. Additionally, particle accumulation on leaf surfaces adversely affected the stomatal conductance of plant leaves, leading to reductions in both the transpiration and photosynthetic rates. This study provides insights into the impact and role of plants from different habitats in mitigating urban particulate pollution. [ABSTRACT FROM AUTHOR]

In order to evaluate the impact of olive activities on the population health in Fez rural, which is affected by air pollution, a retrospective study of acute respiratory infections (ARI) and chronic was studied (about ten years) in the commune of Ain Taoujdate (30 km of Fez center), known by its strong pollution particularly that of the air caused by industrial activity. Method using was the examination of medical records from 2004 to 2015. Analyzing the evolution of respiratory infections at Ain Taoujdate, we noticed a remarkable and repetitive increase, during olive tree campaign and in the spring (in each year), following the air pollution in the commune and abundance of pollen (pollinosis). Very high cases of pneumopathies recorded during the month of December 2004 and which reached 305 and 124 cases successively and which corresponds to the most important triturating activity during these twelve years of study. On the other hand the asthma crisis of Ain Taoujdate, shows a very large value in olive oil milling season. The main cause seems to be air pollution of Ain Taoujdate, since a large oil mill that releases a huge amount of fume is installed close to the population. Statistical analyzes using principal component analysis (PCA) and ascending hierarchical classification (HAC), were able to show an eventual correlation. This work excludes microbial infections; its limitation is the absence of air quality data and was the consequences to take stock of the impact of air pollution caused by oil mills on respiratory health. [ABSTRACT FROM AUTHOR]

Given that 60% of Ukraine's population resides in urban areas, socio-economic and environmental modernization of the country's regions is indispensable for sustainable development. This study addresses the factors characterized by various challenges that impede the sustainable development of urbanized territories in Ukraine. The aim of this research is to analyze the conditions necessary for ensuring sustainable development, using the Odesa industrial-urban agglomeration as a case study. The methodological framework of this article relies on a critical analysis of existing assessments of the conditions for sustainable development in urbanized territories of Ukraine, alongside our own research findings on this issue. Preservation of a favorable natural environment and efficient resource utilization are fundamental principles underpinning the sustainable development of urbanized areas. To actualize these principles, a comprehensive set of measures is proposed to enhance the quality of the air basin, natural waters, soil cover, geological environment, and urban biocenosis. Additionally, an effective waste management system is advocated. Furthermore, strategies for conserving and expanding green spaces, which play a pivotal role in mitigating the adverse effects of urbanization on the environment, enhancing air quality, and improving the well-being of urban residents, are delineated. Horizontal and vertical landscaping initiatives further enhance the aesthetic appeal of the city. [ABSTRACT FROM AUTHOR]

Air quality concerns have become increasingly serious in metropolises such as Tehran (Iran) in recent years. This study aims to assess the contribution of urban trees in Tehran toward mitigating air pollution and to evaluate the economic value of this ecosystem service using the i‐Tree Eco model. To accomplish this objective, we utilized Tehran's original land use map, identifying five distinct land use categories: commercial and industrial, parks and urban forests, residential areas, roads and transportation, and urban services. Field data necessary for this analysis were collected from 316 designated plots, each with a radius of 11.3 m, and subsequently analyzed using the i‐Tree Eco model. The locations of these plots were determined using the stratified sampling method. The results illustrate that Tehran's urban trees removed 1286.4 tons of pollutants in 2020. Specifically, the annual rates of air pollution removal were found to be 134.8 tons for CO; 299.7 tons for NO2; 270.3 tons for O3; 0.7 tons for PM2.5; 489.4 tons for PM10 (particulate matter with a diameter size between 2.5 and 10 µm); and 91.5 tons for SO2, with an associated monetary value of US$1 536 619. However, despite this significant removal capacity, the impact remains relatively small compared with the total amount of pollution emitted in 2020, accounting for only 0.17%. This is attributed to the high emissions rate and low per capita green space in the city. These findings could serve as a foundation for future research and urban planning initiatives aimed at enhancing green spaces in urban areas, thereby promoting sustainable urban development. Integr Environ Assess Manag 2024;20:2142–2152. © 2024 SETAC Key Points: Tehran's urban trees removed 1286 tons of pollutants in 2020, and yet their contribution toward improving air quality was relatively small, accounting for less than 1% of the overall pollution reduction and unable to offset local emissions due to dormancy in winter.Compared with other preferred approaches, such as replacing eroded diesel buses and trucks with newer models, urban trees showed the lowest cost‐effectiveness in improving air quality but were insufficient on their own.Urban planners and managers must consider the combined impact of urban trees with other approaches to significantly mitigate air pollution and ensure city sustainability.Future research should investigate the role of shrubs alongside trees in reducing air pollution and quantify the combined air pollution removal by vegetation canopies to fully understand urban green infrastructure's effectiveness in mitigating air pollution. [ABSTRACT FROM AUTHOR]

Low-cost particulate matter sensors (LCS) are vital for improving the spatial and temporal resolution of air quality data, supplementing sparsely placed official monitoring stations. Despite their benefits, LCS readings can be biased due to the physical properties of aerosol particles and device limitations. An optimization model is essential to enhance LCS data accuracy. This paper presents a calibration study of the LCS network of Timișoara, Romania. The calibration began by selecting LCS devices near National Air Quality Monitoring Network (NAQMN) stations and developing parametric models, choosing the best for broader application. Plantower, Sensirion, and Honeywell sensors showed comparable accuracy. Calibration involved clusters within a 750 m radius around NAQMN stations. Models incorporating RH corrections and multiple linear regression (MLR) were fitted. The best model was validated against data from unseen sensors, leading to mean bias errors (MBE) within 9-17% and RMSEs of 33-35%, within sensor uncertainty margins. Applied to the city-wide LCS network, the model identified several stations regularly exceeding the EU daily PM10 threshold, unnoticed by NAQMN stations due to their limited coverage. The study highlights the necessity of granular monitoring to accurately capture urban air quality variations. [ABSTRACT FROM AUTHOR]

Poor indoor air quality is one of the factors that can cause health problems. Tuberculosis (TB) and acute respiratory infections (ARIs) are among the diseases often associated with indoor air quality. In 2022, the highest number of TB cases was recorded in Surabaya, with 4739 cases. The research location, at the Wonokusumo Public Health Center, still reported a high number of TB cases, with 126 cases in 2022 and 84 cases in 2023. The use of Cyclone Ventilator Modification (CVM) has shown to improve the indoor air quality of occupants. This study aims to analyze the capability and the time required for the CVM to enhance the indoor air quality in homes of TB and ARI patients, using indicators such as temperature, humidity, airborne microbiology, air flow rate, PM2.5 dust particles, and lighting. This experimental study uses a One Group Pre‐Test Post‐Test Design to test changes following the experiment. The device operates on the principle of improving air quality, including temperature, humidity, air flow rate, lighting, and the ability to reduce airborne microbiology and PM2.5 dust particles indoors. The effectiveness of the CVM in improving indoor air quality for TB and ARI patients is analyzed using a paired T‐test analysis, revealing significant differences in indoor air quality indicators before and after the device's operation. The results obtained show that the two‐tailed significance value is less than 0, indicating a significant difference between the initial and final variables. After CVM operation in five homes with eight replications, the CVM's effectiveness was optimal in relation to room area/volume. This study was conducted as a simple step to minimize pollutants and improve indoor air quality. These findings can be applied and utilized to create healthier buildings or homes. [ABSTRACT FROM AUTHOR]

This research was carried out to monitor the NO2 and SO2 (Nitrogen and Sulfur IV oxides) levels around the Oben gas flow station in Edo State, Southern Nigeria, using remote sensing data. Secondary data was collected from the Sentinel 5P satellite and processed using Google Earth Pro, ArcMap, Google Earth Engine, and Microsoft Excel to determine the concentrations of the pollutants of interest in the study area for 2019 and 2020. In 2019 and 2020, the maximum mean values for SO2 were 3.99 E-5 mol/m2 and 4.26 E-5 mol/m2, respectively, and for NO2, the maximum mean values were 6.63 E-5 mol/m2 and 6.88 E-5 mol/m2, respectively. For the seasonal variations in concentrations of pollutants, there was p > 0.05 (no significant differences) in the seasonal variation between the concentrations of SO2 in 2019 (t (5) = 1.410) and 2020 (t (5) = 2.399). There was a significant difference (p < 0.05) between NO2 concentrations in the wet and dry seasons for 2019 (t (5) = 5.719) and 2020 (t (5) = 5.991). Also, there was a significant variation between the concentrations of NO2 in 2019 and 2020 at p > 0.05, but not for SO2. Based on the findings from this study, it is recommended that stricter enforcement of already existing legislation on gas flaring and finding cleaner or alternative sources of energy like biofuels and biogas, are highly needed to reduce any unforeseen health and environmental impact in this zone. [ABSTRACT FROM AUTHOR]

The ability of a low-carbon strategy to promote tourism development is critical to achieving economic development and environmental protection. Taking China's low-carbon city pilot (LCCP) policy as a quasi-natural experiment, this study evaluates the low-carbon strategy's effect on tourism development by adopting the difference-in-difference method with 275 cities'panel data from 2002 to 2019. The results verify that tourism has significantly developed, driven by the LCCP policy, and this finding is robust even after multiple tests. Mechanism and heterogeneity analyses show that LCCP policy drives tourism development by improving air quality, promoting industrial structure upgrading, and enhancing green technology innovation. Additionally, the pilot construction has a positive spatial spillover impact on the tourism industry in surrounding cities, reflecting the demonstration effect of the pilot city on the neighbouring cities. [ABSTRACT FROM AUTHOR]

Multi-site PM2.5 prediction has emerged as a crucial approach, given that the accuracy of prediction models based solely on data from a single monitoring station may be constrained. However, existing multi-site PM2.5 prediction methods predominantly rely on recurrent networks for extracting temporal dependencies and overlook the domain knowledge related to air quality pollutant dispersion. This study aims to explore whether a superior prediction architecture exists that not only approximates the prediction performance of recurrent networks through feedforward networks but also integrates domain knowledge of PM2.5. Consequently, we propose a novel spatio-temporal attention causal convolutional neural network (Causal-STAN) architecture for predicting PM2.5 concentrations at multiple sites in the Yangtze River Delta region of China. Causal-STAN comprises two components: a multi-site spatiotemporal feature integration module, which identifies temporal local correlation trends and spatial correlations in the spatio-temporal data, and extracts inter-site PM2.5 concentrations from the directional residual block to delineate directional features of PM2.5 concentration dispersion between sites; and a temporal causal attention convolutional network that captures the internal correlation information and long-term dependencies in the time series. Causal-STAN was evaluated using one-year data from 247 sites in mainland China. Compared to six state-of-the-art baseline models, Causal-STAN achieves optimal performance in 6-hour future predictions, surpassing the recurrent network model and reducing the prediction error by 8%-10%. [ABSTRACT FROM AUTHOR]

The air quality index (AQI) is a commonly employed metric for evaluating air quality across diverse locations and temporal spans. Similar to other environmental datasets, AQI data can exhibit outliers data points markedly divergent from the norm, signifying instances of exceptionally favorable or adverse air quality. This becomes crucial in identifying and comprehending severe pollution episodes with far‐reaching environmental and public health implications. This study utilizes air quality data from January 1, 2014, to January 31, 2021, collected at daily intervals in Shanghai City, China, as the experimental dataset. The dataset includes daily AQI measurements, along with six pollutant concentrations: particulate matter (PM2.5 and PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO). Each pollutant's concentration is measured in micrograms per cubic meter (μ$$ \upmu $$g/m 3$$ {}^3 $$). The dataset is then preprocessed by cleaning and normalizing it before using K‐means clustering to discover different patterns. A stacked ensemble machine learning model that incorporates K‐means clustering, random forest (RF) and gradient boosting classifier (GBC) is developed and compared to decision tree, support vector machine, K‐nearest neighbor and Naive Bayes algorithms to evaluate its performance in identifying outliers using accuracy, precision, recall, and F1‐score. The stacked model outperformed all other established models based on the accuracy, precision, recall, and F1‐score of 0.99, 0.99, 0.97, and 0.99, respectively. [ABSTRACT FROM AUTHOR]

This study examines the validation and spatial–temporal variability of simulated PM10 and PM2.5 concentrations over Ahmedabad city using the WRF-Chem model with EDGAR global emissions and locally developed PM emissions as inputs. The validation process involves comparing simulated PM10, PM2.5, and meteorological parameters with in-situ measurements from stations at Pirana and S P Stadium during 16th-17th May and 16th-17th December 2018. The analysis focuses on six-hourly averaged data, highlighting the significant role of Wind Speed, Wind Direction, and Planetary Boundary Layer Height (PBLH) in the dispersion of air pollutants. The results show that the model using locally developed PM emissions significantly reduces bias and provides better spatial distribution patterns compared to EDGAR emissions. For instance, in May, the average simulated PM10 using EDGAR emissions was 85 µg/m3, while the locally developed emissions yielded an average of 183 µg/m3, closer to the observed in-situ average of 178 µg/m3. Similarly, in December, the simulated PM10 using EDGAR emissions was 97 µg/m3 compared to 232 µg/m3 from local emissions, with an in-situ average of 147 µg/m3. The spatial analysis reveals that during May, 52% of the western areas experienced 'Good' air quality in the morning, decreasing to 40% by the evening. In contrast, December showed more severe pollution, with 45% of the North–North Western city experiencing 'Moderately Polluted' air quality by evening. The correlation coefficients (R2) for PM10 validation at Pirana and S P Stadium were 0.73 and 0.93 respectively in May using EDGAR emissions, improving to 0.77 and 0.91 with local emissions. This study underscores the need for improved emission inventories and mitigation strategies to enhance air quality in Ahmedabad. The significant seasonal variations and the impact of meteorological parameters on pollutant dispersion highlight the importance of localized data and targeted interventions for effective air quality management. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the effects of air quality on housing prices from the short-run and long-run perspectives. We utilize monthly housing price data and the particulate matter 2.5 (PM2.5) index for prefecture-level cities in China. Relying on instrumental variable (IV) estimation and dynamic common correlated effects-pooled mean group (DCCE-PMG) techniques, our results reveal a short-run negative causal and a long-run inverted-U relationship between air pollution and housing prices. This finding suggests that the effect of anti-pollution efforts on housing prices is nonmonotone; initially having a favorable effect and then after reaching a certain turning point, the pollution reduction effort is associated with depression in housing prices. We further find that the heterogeneity in the points of different cities can be explained by socio-demographic factors. Specifically, our results reveal that while pollution-reduction campaign initially had effects related to willingness-to-pay (WTP) theory, further control measures had an adverse effect on housing prices. Thus, our study presents important implications for the effectiveness of environmental regulations. [ABSTRACT FROM AUTHOR]

Air quality information disclosure has emerged as a popular policy tool to reduce emissions, yet its impact on both environmental and economic performance remains ambiguous. This study employs a comprehensive measure of environmental-economic efficiency, the Green Total Factor Productivity (GTFP), to investigate the effect of China's mandated air quality disclosure program from 2003 to 2016. Using a difference-in-differences approach, we find that cities subject to disclosure experienced an average decline of 8% in GTFP. To further explore the heterogeneity in the treatment effect, we apply causal forest, a state-of-the-art causal machine learning technique for estimating individual treatment effects. The analysis uncovers substantial variation in the impact of information disclosure across cities, suggesting that the negative average effect may be partially attributed to mistargeting. We identify financial constraints, industrial composition, and urban scale as key moderators of the disclosure program's effectiveness. Moreover, our findings indicate that disclosing negative information, such as severe pollution levels or low environmental rankings, has a more pronounced impact compared to neutral content. By identifying key moderators and differential impacts of disclosure content, this study provides a foundation for targeted policy design to enhance the effectiveness of environmental information regulations. [ABSTRACT FROM AUTHOR]

This study investigates the impact of short-term air quality fluctuations on young people's intentions to migrate for higher education in Beijing, using Baidu search behavior as a proxy for interest in local versus non-local universities. To underpin causal inference, OLS panel estimates are supplemented by instrumental variables estimates that exploit variations in air quality driven by plausibly exogenous variations in temperature inversions. The results demonstrate that when monthly air quality in Beijing moves from 5th (excellent-day level) to 95th (moderately-polluted level) percentile, search for local education decreases by 2.51% under OLS and 8.23% under IV. Our findings provides a new perspective on how environmental factors shape educational mobility aspirations based on the causal effects, and offers policymakers insights into improving environmental quality to attract and retain talent. [ABSTRACT FROM AUTHOR]

Community science has increased in popularity in communities where residents hope to investigate the relationship between environmental issues and personal health. This study partnered with neighborhoods in the most polluted residential zip code in the US to conduct community science air quality monitoring. We conducted 60 semi-structured interviews after two monitoring deployments to understand participants' subjective experiences of pollution exposure, their engagement with low-cost air quality monitors, and their data interpretation. We utilize the environmental health concept 'exposure experience' to analyze how participants use personal monitors, understand their data, and reinterpret their pollution exposure as a result. We further explore how participants' understandings are circumscribed by the technological features of low-cost monitors. We find that participants adopt both protective and mitigating behavioral changes based on information gained from personal experiments and hypothesis testing while using the monitors. Of their own accord, 40% of participants in this study adopted mitigation behaviors after identifying sources that impacted their personal air quality. Our analysis reveals that real-time data accessibility through low-cost monitors builds exposure awareness and enables residents of environmental justice communities to test, validate, or invalidate sensory experiences and challenge existing assumptions. These findings point to specific pathways for using low-cost monitors to support individual decisionmaking and contribute to behavioral change. Findings also identify some limitations of low-cost monitors; designers of low-cost monitors should consider how composite Air Quality Scores may encourage community scientists to equally value scientifically-established pollutants (e.g., PM) with less scientifically-established pollutants (e.g., TVOCs), without additional scientific training and health-related information. [ABSTRACT FROM AUTHOR]

AbstractDuring the dry season, the northern region of Thailand consistently grapples with compromised air quality, notably an excessive level of particulate matter with a diameter of less than 2.5 microns (PM2.5), which poses implications for public health. Wind speed emerges as the primary factor that diminishes the concentration of PM2.5, making the evaluation of wind speed changes crucial in understanding and potentially mitigating the impact of air pollution on public health in the region. The wind speed data contain zero and positive values. This can be estimated through the confidence intervals of the difference and ratio between two means of delta-Birnbaum-Saunders distributions using various methods, including the generalized confidence interval (GCI), bootstrap confidence interval (BCI), and generalized fiducial confidence interval (GFCI). These methods rely on variance stabilizing transformations (VST), the Wilson and Hannig methods. According to the simulation, for small sample sizes, the GCI based on the VST and Hannig was the most effective method in terms of coverage probabilities and average lengths. For medium sample sizes, it was found that the performance of GFCI based on the VST method was superior to other methods. Additionally, when confronted with a large sample size, the BCI based on the VST method becomes relevant and will be introduced. To demonstrate the efficacy of our proposed methods, we applied them to wind speed data gathered from Chiang Mai Airport station in Thailand from January to February in both 2022 and 2023. [ABSTRACT FROM AUTHOR]

We present top-down global gridded emissions of NOx for the year 2022. This dataset is constructed from retrievals of tropospheric vertical column densities of NO2 by the TROPOMI spaceborne instrument associated with winds and atmospheric composition data from ECMWF reanalyses, using an improved version of a mass-balance atmospheric inversion. The emissions are provided with a spatial resolution of 0.0625°×0.0625° and deliver a detailed overview of the distribution of emissions. They allow the identification of intense area sources and isolated emitters, and the quantification of their associated emissions. At global level, the emissions obtained are consistent with the EDGARv6.1 bottom-up inventory, although there are differences at regional level, particularly in emerging countries and countries with low observation densities. The three largest emitting countries, China, the United States and India, are 11, 16 and 6 % lower than EDGAR estimates. Uncertainties remain high, and a quantitative analysis of emissions over several averaging periods indicates that averaging emissions uniformly across the year may be sufficient to obtain estimates consistent with annual averages, in regions of the world with high retrieval densities. This dataset is designed to be updated with a low latency to help policymakers monitor emissions and implement energy savings and clean air quality policies. The data can be accessed at https://doi.org/10.5281/zenodo.13957837 as monthly files (Rey-Pommier et al., 2024). [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusion Emerging evidence has underscored the harmful effects of air pollution on the upper airway. We investigated the relationship between ambient particulate matter (PM) level and the frequency of outpatient visits for chronic rhinosinusitis (CRS).We conducted an ecological cohort study of US adults enrolled in The Merative MarketScan outpatient database from 2007 to 2020. For each geographical subunit (core‐based statistical area [CBSA]), we calculated the annual rate of CRS‐related outpatient visits per 1000 well‐patient checkup visits (CRS‐OV). Using data from the Environmental Protection Agency's Air Quality System, we mapped the rolling statistical average of daily PM2.5 and PM10 over the preceding year onto each CBSA × year combination. We employed multivariable negative binomial regression modeling to estimate the association between PM levels and subsequent CRS‐OV.Across 3933 observations (CBSA × year combinations), encompassing ∼4 billion visits, the median CRS‐OV was 164 (interquartile range 110–267). The mean PM2.5 level was 8.9 µg/m3 (SD 2.6) and the mean PM10 level was 20.2 µg/m3 (SD 7.2). Adjusting for patient demographics and respiratory comorbidities, a compounded rise in subsequent CRS‐OV was observed with increasing PM levels. Each µg/m3 rise in PM2.5 independently predicted a 10% increase in CRS‐OV (adjusted incidence rate ratio [aIRR]) 1.10, 95% confidence interval [CI] 1.08–1.13) and each µg/m3 rise in PM10 independently predicted a 3% increase in CRS‐OV (aIRR 1.03, 95% CI 1.02–1.04).Elevated ambient PM2.5 and PM10 levels are associated with a subsequent compounded increase in the frequency of CRS‐OV, with PM2.5 predicting a more pronounced rise compared to PM10. [ABSTRACT FROM AUTHOR]

Air pollution in the interior of our homes is caused by diverse chemical, physical, and biological entities. This review comprehensively explores the current understanding of sources and health impacts of gaseous and particulate pollutants. Trend analysis of indoor air research worldwide revealed a quantum jump of 2.8 times in the number of publications during the last ten years. Indoor air pollutants are innumerable, but only a few are widely prevalent in most households. The qualitative complexity of pollutants translates to different health problems, including respiratory diseases, cardiovascular conditions, cancer, and deaths. There exist wide-scale disparities in the negative impacts among different economic strata, genders, and age groups; children and elderly populations are more vulnerable. In developing countries, pollutants primarily arise from traditional sources, whereas in developed countries, pollutants from non-conventional sources are comparatively significant. Only a few countries have indoor air regulations, policies, monitoring plans and effective enforcement. [ABSTRACT FROM AUTHOR]