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Wildfires have become an important source of particulate matter (PM2.5 < 2.5-μm diameter), leading to unhealthy air quality index occurrences in the western United States. Since people mainly shelter indoors during wildfire smoke events, the infiltration of wildfire PM2.5 into indoor environments is a key determinant of human exposure and is potentially controllable with appropriate awareness, infrastructure investment, and public education. Using time-resolved observations outside and inside more than 1,400 buildings from the crowdsourced PurpleAir sensor network in California, we found that the geometric mean infiltration ratios (indoor PM2.5 of outdoor origin/outdoor PM2.5) were reduced from 0.4 during non-fire days to 0.2 during wildfire days. Even with reduced infiltration, the mean indoor concentration of PM2.5 nearly tripled during wildfire events, with a lower infiltration in newer buildings and those utilizing air conditioning or filtration.

This paper aims to investigate the potential contribution of biomass burning in PM2.5 pollution in Northern Thailand. We applied the coupled atmospheric and air pollution model which is based on the Weather Research and Forecasting Model (WRF) and a Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT). The model output was compared to the ground-based measurements from the Pollution Control Department (PCD) to examine the model performance. As a result of the model evaluation, the meteorological variables agreed well with observations using the Index of Agreement (IOA) with ranges of 0.57 to 0.79 for temperature and 0.32 to 0.54 for wind speed, while the fractional biases of temperature and wind speed were 1.3 to 2.5 °C and 1.2 to 2.1 m/s. Analysis of the model and hotspots from the Moderate Imaging Spectroradiometer (MODIS) found that biomass burning from neighboring countries has greater potential to contribute to air pollution in northern Thailand than national emissions, which is indicated by the number of hotspot locations in Burma being greater than those in Thailand by two times under the influence of two major channels of Asian Monsoons, including easterly and northwesterly winds that bring pollutants from neighboring counties towards northern Thailand.

In modern society, air pollution is an important topic as this pollution exerts a critically bad influence on human health and the environment. Among air pollutants, Particulate Matter (PM2.5) consists of suspended particles with a diameter equal to or less than 2.5 μm. Sources of PM2.5 can be coal-fired power generation, smoke, or dusts. These suspended particles in the air can damage the respiratory and cardiovascular systems of the human body, which may further lead to other diseases such as asthma, lung cancer, or cardiovascular diseases. To monitor and estimate the PM2.5 concentration, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are combined and applied to the PM2.5 forecasting system. To compare the overall performance of each algorithm, four measurement indexes, Mean Absolute Error (MAE), Root Mean Square Error (RMSE) Pearson correlation coefficient and Index of Agreement (IA) are applied to the experiments in this paper. Compared with other machine learning methods, the experimental results showed that the forecasting accuracy of the proposed CNN-LSTM model (APNet) is verified to be the highest in this paper. For the CNN-LSTM model, its feasibility and practicability to forecast the PM2.5 concentration are also verified in this paper. The main contribution of this paper is to develop a deep neural network model that integrates the CNN and LSTM architectures, and through historical data such as cumulated hours of rain, cumulated wind speed and PM2.5 concentration. In the future, this study can also be applied to the prevention and control of PM2.5.

Agricultural stubble burning is the third largest source of air pollution after vehicular and industrial emissions. Fine particulate matter (PM2.5), volatile organic compounds (VOCs), carbon monoxide (CO), nitrogen dioxide (NO2), and black carbon (BC) are some of the pollutants emitted during such burning events. The Lower Rio Grande Valley (RGV) region of South Texas is a major hub of agricultural activity, and sugarcane farming is one of them. Unfortunately, this activity results in episodic events of high air pollution in this low-resourced, Hispanic/Latino majority region of the U.S.–Mexico border. This study presents results from a sugarcane site in La Feria, South Texas, where the air quality was monitored before, during, and after the sugarcane stubble burning. Various parameters were monitored on an hourly basis from 24 February 2022 to 4 April 2022. Our results demonstrate high levels of all the monitored pollutants during the burning phase in contrast to the pre- and post-burning period. The black carbon levels went up to 6.43 µg m−3 on the day of burning activity. An increase of 10%, 11.6%, 25.29%, 55%, and 67.57% was recorded in the PM1, PM2.5, PM10, Black Carbon, and CO levels, respectively, during the burning period in comparison with the total study period. The absorption Ångström exponent value reached a maximum value of 2.03 during the burning activity. ThePM2.5/PM10 ratio was 0.87 during the burning activity. This study also highlights the importance for continuous monitoring of air quality levels due to stubble burning in the Lower Rio Grande Valley Region of South Texas.

Fine particulate matters (PM2.5) and relative humidity (RH) in the ambient atmosphere are the leading anthropogenic and natural factors changing atmospheric horizontal visibility. Based on the analysis of environmental and meteorological data observed over 2013–2019 in Nanjing, an urban area in East China, this study investigated the influences of PM2.5 and RH on atmospheric visibility changes over recent years. The visibility had significantly negative correlations with the PM2.5 concentrations and RH changes. The nonlinear relationships existed between PM2.5 concentrations and visibility, as well as between RH and visibility, with the inflection points in the atmospheric visibility changes. The PM2.5 inflection concentrations were 81.0 μg m−3, 76.0 μg m−3, 49.0 μg m−3, and 33.0 μg m−3, respectively, for the RH ranges of RH < 60%, 60% ≤ RH < 80%, 80% ≤ RH < 90%, and RH ≥ 90%, indicating that the improvement of visibility with reducing PM2.5 concentrations could be more difficult under the humid meteorological condition. The visibility changes were most sensitive to PM2.5 concentrations in the RH range of 60–80% in this urban area of East China. The relative contributions of natural factor RH and anthropogenic factor PM2.5 to variations of wintertime atmospheric visibility were quantified with 54.3% and 45.7%, respectively, revealing an important role of natural factor RH in the change of atmospheric visibility in the urban area of East Asian monsoon region.

China has been experiencing fine particle (i.e., aerodynamic diameters ≤ 2.5 μm; PM2.5) pollution and acid rain in recent decades, which exert adverse impacts on human health and the ecosystem. Recently, ammonia (i.e., NH₃) emission reduction has been proposed as a strategic option to mitigate haze pollution. However, atmospheric NH₃ is also closely bound to nitrogen deposition and acid rain, and comprehensive impacts of NH₃ emission control are still poorly understood in China. In this study, by integrating a chemical transport model with a high-resolution NH₃ emission inventory, we find that NH₃ emission abatement can mitigate PM2.5 pollution and nitrogen deposition but would worsen acid rain in China. Quantitatively, a 50% reduction in NH₃ emissions achievable by improving agricultural management, along with a targeted emission reduction (15%) for sulfur dioxide and nitrogen oxides, can alleviate PM2.5 pollution by 11−17% primarily by suppressing ammonium nitrate formation. Meanwhile, nitrogen deposition is estimated to decrease by 34%, with the area exceeding the critical load shrinking from 17% to 9% of China’s terrestrial land. Nevertheless, this NH₃ reduction would significantly aggravate precipitation acidification, with a decrease of as much as 1.0 unit in rainfall pH and a corresponding substantial increase in areas with heavy acid rain. An economic evaluation demonstrates that the worsened acid rain would partly offset the total economic benefit from improved air quality and less nitrogen deposition. After considering the costs of abatement options, we propose a region-specific strategy for multipollutant controls that will benefit human and ecosystem health.

Scientific evidence spanning experimental and epidemiologic studies has shown that air pollution exposures can lead to a range of health effects. Quantitative approaches that allow for the estimation of the adverse health impacts attributed to air pollution enable researchers and policy analysts to convey the public health impact of poor air quality. Multiple tools are currently available to conduct such analyses, which includes software packages designed by the World Health Organization (WHO): AirQ+, and the U.S. Environmental Protection Agency (U.S. EPA): Environmental Benefits Mapping and Analysis Program—Community Edition (BenMAP—CE), to quantify the number and economic value of air pollution-attributable premature deaths and illnesses. WHO’s AirQ+ and U.S. EPA’s BenMAP—CE are among the most popular tools to quantify these effects as reflected by the hundreds of peer-reviewed publications and technical reports over the past two decades that have employed these tools spanning many countries and multiple continents. Within this paper we conduct an analysis using common input parameters to compare AirQ+ and BenMAP—CE and show that the two software packages well align in the calculation of health impacts. Additionally, we detail the research questions best addressed by each tool.

Observing the spatial heterogeneities of NO2 air pollution is an important first step in quantifying NOX emissions and exposures. This study investigates the capabilities of the Tropospheric Monitoring Instrument (TROPOMI) in observing the spatial and temporal patterns of NO2 pollution in the continental United States. The unprecedented sensitivity of the sensor can differentiate the fine‐scale spatial heterogeneities in urban areas, such as emissions related to airport/shipping operations and high traffic, and the relatively small emission sources in rural areas, such as power plants and mining operations. We then examine NO2 columns by day‐of‐the‐week and find that Saturday and Sunday concentrations are 16% and 24% lower respectively, than during weekdays. We also analyze the correlation of daily maximum 2‐m temperatures and NO2 column amounts and find that NO2 is larger on the hottest days (>32°C) as compared to warm days (26°C–32°C), which is in contrast to a general decrease in NO2 with increasing temperature at moderate temperatures. Finally, we demonstrate that a linear regression fit of 2019 annual TROPOMI NO2 data to annual surface‐level concentrations yields relatively strong correlation (R2 = 0.66). These new developments make TROPOMI NO2 satellite data advantageous for policymakers and public health officials, who request information at high spatial resolution and short timescales, in order to assess, devise, and evaluate regulations. Plain Language Summary Nitrogen oxides are a group of air pollutants released after fossil fuel combustion. A constituent of nitrogen oxides, nitrogen dioxide (NO2), can be observed by satellite instruments due to its chemical properties. In this project, we average together images of NO2 pollution gathered by the Tropospheric Monitoring Instrument satellite instrument over the United States in order to better determine the spatial distribution of NO2 air pollution. We find that this newest satellite instrument can observe air pollution with unprecedented clarity, similar to how HDTV is an advancement over regular TV. For example, we quantify pollution near individual airports, shipping areas, and major interstates; previous satellite instruments were unable to quantify air pollution with this type of precision. We also average the satellite data over different intervals to better determine cycles of air pollution. We find that NO2 air pollution is 16% lower on Saturdays and 24% lower on Sundays. Additionally, we find that NO2 pollution is larger on the hottest summer days as compared to typical summer days. These developments demonstrate how this new satellite instrument can advantageous for policymakers and health officials, who request information at high spatial resolution and short timescales, in order to assess, devise, and evaluate regulations Key Points The high instrument sensitivity of Tropospheric Monitoring Instrument (TROPOMI) can measure NO2 pollution with unprecedented clarity compared to predecessor instruments We can now quantify pollution hotspots within cities such as those related to airport/shipping operations and high traffic areas Annual column NO2 observed by TROPOMI has good correlation (R2 = 0.66) with EPA surface observations without any surface‐to‐column conversion

It is always a challenge to see the deposition of single PM 2.5 particles, the harmful inhalable aerosols in the polluted air, in alveolar region, and their invasion to the extrapulmonary organs. The dynamic deposition process and the nonuniform deposition pattern of PM 2.5 in the alveolar region are revealed using a fluorescent imaging method with high temporal and spatial resolutions. This observation technology would also bring insight in the study of the public health in air pollution and lung administration. The deposition of PM 2.5 (fine particulate matter in air with diameter smaller than 2.5 μm) in lungs is harmful to human health. However, real-time observation on the deposition of particles in the acinar area of the lung is still a challenge in experiments. Here, a fluorescent imaging method is developed to visualize the deposition process with a high temporal and spatial resolution. The observations reveal that the deposition pattern is nonuniform, and the maximum deposition rate in the acinar area differs significantly from the prediction of the widely used average deposition model. The method is also used to find single particles in the kidney and liver, though such particles are commonly believed to be too large to enter the extrapulmonary organs.

Nearly 60% of U.S. children live in counties with particulate matter less than or equal to 2.5 μm in aerodynamic diameter (PM ) concentrations above air quality standards. Understanding the relationship between ambient air pollution exposure and health outcomes informs actions to reduce exposure and disease risk. To evaluate the association between ambient PM levels and healthcare encounters for acute lower respiratory infection (ALRI). Using an observational case-crossover design, subjects (n = 146,397) were studied if they had an ALRI diagnosis and resided on Utah's Wasatch Front. PM air pollution concentrations were measured using community-based air quality monitors between 1999 and 2016. Odds ratios for ALRI healthcare encounters were calculated after stratification by ages 0-2, 3-17, and 18 or more years. Approximately 77% (n = 112,467) of subjects were 0-2 years of age. The odds of ALRI encounter for these young children increased within 1 week of elevated PM and peaked after 3 weeks with a cumulative 28-day odds ratio of 1.15 per +10 μg/m (95% confidence interval, 1.12-1.19). ALRI encounters with diagnosed and laboratory-confirmed respiratory syncytial virus and influenza increased following elevated ambient PM levels. Similar elevated odds for ALRI were also observed for older children, although the number of events and precision of estimates were much lower. In this large sample of urban/suburban patients, short-term exposure to elevated PM air pollution was associated with greater healthcare use for ALRI in young children, older children, and adults. Further exploration is needed of causal interactions between PM and ALRI.