Relationships of relative humidity with PM2.5 and PM10 in the Yangtze River Delta, China

Probing the capacity of a spatiotemporal deep learning model for short-term PM2.5 forecasts in a coastal urban area

Accurate forecast of fine particulate matter (PM2.5) is crucial for city air pollution control, yet remains challenging due to the complex urban atmospheric chemical and physical processes. Recently deep learning has been routinely applied for better urban PM2.5 forecasts. However, their capacity to represent the spatiotemporal urban atmospheric processes remains underexplored, especially compared with traditional approaches such as chemistry-transport models (CTMs) and shallow statistical methods other than deep learning. Here we probe such urban-scale representation capacity of a spatiotemporal deep learning (STDL) model for 24-hour short-term PM2.5 forecasts at six urban stations in Rizhao, a coastal city in China. Compared with two operational CTMs and three statistical models, the STDL model shows its superiority with improvements in all five evaluation metrics, notably in root mean square error (RMSE) for forecasts at lead times within 12 h with reductions of 49.8 % and 47.8 % respectively. This demonstrates the STDL model's capacity to represent nonlinear small-scale phenomena such as street-level emissions and urban meteorology that are in general not well represented in either CTMs or shallow statistical models. This gain of small-scale representation in forecast performance decreases at increasing lead times, leading to similar RMSEs to the statistical methods (linear shallow representations) at about 12 h and to the CTMs (mesoscale representations) at 24 h. The STDL model performs especially well in winter, when complex urban physical and chemical processes dominate the frequent severe air pollution, and in moisture conditions fostering hygroscopic growth of particles. The DL-based PM2.5 forecasts align with observed trends under various humidity and wind conditions. Such investigation into the potential and limitations of deep learning representation for urban PM2.5 forecasting could hopefully inspire further fusion of distinct representations from CTMs and deep networks to break the conventional limits of short-term PM2.5 forecasts.

Investigating the impact of extreme weather events and related indicators on cardiometabolic multimorbidity

BACKGROUND

The impact of weather on human health has been proven, but the impact of extreme weather events on cardiometabolic multimorbidity (CMM) needs to be urgently explored.

OBJECTIVES

Investigating the impact of extreme temperature, relative humidity (RH), and laboratory testing parameters at admission on adverse events in CMM hospitalizations.

DESIGNS

Time-stratified case-crossover design.

METHODS

A distributional lag nonlinear model with a time-stratified case-crossover design was used to explore the nonlinear lagged association between environmental factors and CMM. Subsequently, unbalanced data were processed by 1:2 propensity score matching (PSM) and conditional logistic regression was employed to analyze the association between laboratory indicators and unplanned readmissions for CMM. Finally, the previously identified environmental factors and relevant laboratory indicators were incorporated into different machine learning models to predict the risk of unplanned readmission for CMM.

RESULTS

There are nonlinear associations and hysteresis effects between temperature, RH and hospital admissions for a variety of CMM. In addition, the risk of admission is higher under low temperature and high RH conditions with the addition of particulate matter (PM, PM2.5 and PM10) and O3_8h. The risk is greater for females and adults aged 65 and older. Compared with first quartile (Q1), the fourth quartile (Q4) had a higher association between serum calcium (HR = 1.3632, 95% CI: 1.0732 ~ 1.7334), serum creatinine (HR = 1.7987, 95% CI: 1.3528 ~ 2.3958), fasting plasma glucose (HR = 1.2579, 95% CI: 1.0839 ~ 1.4770), aspartate aminotransferase/ alanine aminotransferase ratio (HR = 2.3131, 95% CI: 1.9844 ~ 2.6418), alanine aminotransferase (HR = 1.7687, 95% CI: 1.2388 ~ 2.2986), and gamma-glutamyltransferase (HR = 1.4951, 95% CI: 1.2551 ~ 1.7351) were independently and positively associated with unplanned readmission for CMM. However, serum total bilirubin and High-Density Lipoprotein (HDL) showed negative correlations. After incorporating environmental factors and their lagged terms, eXtreme Gradient Boosting (XGBoost) demonstrated a more prominent predictive performance for unplanned readmission of CMM patients, with an average area under the receiver operating characteristic curve (AUC) of 0.767 (95% CI:0.7486 ~ 0.7854).

CONCLUSIONS

Extreme cold or wet weather is linked to worsened adverse health effects in female patients with CMM and in individuals aged 65 years and older. Moreover, meteorologic factors and environmental pollutants may elevate the likelihood of unplanned readmissions for CMM.

Short-term effects of PM2.5 components on the respiratory infectious disease: a global perspective

Although previous research has reached agreement on the significant impact of particulate matter (PM2.5) on respiratory infectious diseases, PM2.5 acts as an aggregation of miscellaneous pollutants and the individual effect of each component has not been examined. Here, we investigate the effects of PM2.5 components, including black carbon (BC), organic carbon (OC), sulfate ion (SO4), dust, and sea salt (SS), on the morbidity and mortality of the recent respiratory disease, i.e. COVID-19. The daily data of 236 countries and provinces/states (e.g., in the United States and China) worldwide during 2020-2022 are utilized. To derive the pollutant-specific causal effects, optimal instrumental variables for each pollutant are selected from a large set of atmospheric variables. We find that one µg/m3 increase in OC increases the number of cases and death by about 3% to 6% from the mean worldwide during a lag of one day up to three days. Our findings remain consistent and robust when we change control variables such as the flight index and weather proxies, and also when applying a sine transformation to the positivity and death rate. When analyzing health effects among different areas, we find stronger impact in China, for its higher local OC concentration, as opposed to the impact in the United States. Health benefits from PM2.5 pollution reduction are comparatively high for developed regions, yet decreases in cases and deaths number are rather overt in less developing regions. Our research provides inspiration and reference for dealing with other respiratory diseases in the post-pandemic era.

First temporal distribution model of ambient air pollutants (PM2.5, PM10, and O3) in Yangon City, Myanmar during 2019-2021

Air pollution has emerged as a significant global concern, particularly in urban centers. This study aims to investigate the temporal distribution of air pollutants, including PM2.5, PM10, and O3, utilizing multiple linear regression modeling. Additionally, the research incorporates the calculation of the Air Quality Index (AQI) and Autoregressive Integrated Moving Average (ARIMA) time series modeling to predict the AQI for PM2.5 and PM10. The concentrations and AQI values for PM2.5 ranged from 0 to 93.6 μg/m3 and 0 to 171, respectively, surpassing the Word Health Organization's (WHO) acceptable threshold levels. Similarly, concentrations and AQI values for PM10 ranged from 0.1 to 149.27 μg/m3 and 2-98 μg/m3, respectively, also exceeding WHO standards. Particulate matter pollution exhibited notable peaks during summer and winter. Key meteorological factors, including dew point temperature, relative humidity, and rainfall, showed a significant negative association with all pollutants, while ambient temperature exhibited a significant positive correlation with particulate matter. Multiple linear regression models of particulate matter for winter season demonstrated the highest model performance, explaining most of the variation in particulate matter concentrations. The annual multiple linear regression model for PM2.5 exhibited the most robust performance, explaining 60% of the variation, while the models for PM10 and O3 explained 45% of the variation in their concentrations. Time series modeling projected an increasing trend in the AQI for particulate matter in 2022. The precise and accurate results of this study serve as a valuable reference for developing effective air pollution control strategies and raising awareness of AQI in Myanmar.

The performance of artificial neural networks for modeling daily concentrations of particulate matter from meteorological data

The use of techniques based on artificial intelligence and machine learning for the simulation of many processes is becoming increasingly important in environmental sciences, with applications in the study of time series of atmospheric properties, such as pollution levels. The present work aimed to evaluate the efficiency of a model based on Artificial Neural Networks (ANN) in the simulation PM10 from meteorological data observed between 2018 and 2019 in Guaíba, southern Brazil, thus also having an estimate of the influence of atmospheric conditions on local air pollution. For this purpose, meteorological and PM10 data obtained from the stations Parque 35, sustained by Celulose Riograndense (CMPC), and A-801, sustained by the National Institute of Meteorology (INMET), were used. The ANN used for the simulation was of the Multilayer Perceptron type, trained by the backpropagation algorithm with cross-validation. The results obtained indicate that the simulation was satisfactory with a Nash-Sutcliffe index (NSE) of 0.64, a linear correlation coefficient (R) of 0.81, a relative error (Er) of 26% and a root mean square error (RMSE) of 7.40 µg/m3. Thus, even with some difficulty in estimating extreme concentrations, the model was suitable for the largest range observed, of 10 µg/m3 to 50 µg/m3. For this dataset, the model proved to be an useful assessment tool and has the potential to be applied operationally to contribute to the monitoring and control of air quality levels both in the study area and in other regions of Brazil and the world.

Effects of Gas and Steam Humidity on Particulate Matter Measurements Obtained Using Light-Scattering Sensors

With the increasing need for particulate matter (PM) monitoring, the demand for light-scattering sensors that allow for real-time measurements of PM is increasing. This light-scattering method involves irradiating light to the aerosols in the atmosphere to analyze the scattered light and measure mass concentrations. Humidity affects the measurement results. The humidity in an outdoor environment may exist as gas or steam, such as fog. While the impact of humidity on the light-scattering measurement remains unclear, an accurate estimation of ambient PM concentration is a practical challenge. Therefore, this study investigated the effects of humidity on light-scattering measurements by analyzing the variation in the PM concentration measured by the sensor when relative humidity was due to gaseous and steam vapor. The gaseous humidity did not cause errors in the PM measurements via the light-scattering method. In contrast, steam humidity, such as that caused by fog, resulted in errors in the PM measurement. The results help determine the factors to be considered before applying a light-scattering sensor in an outdoor environment. Based on these factors, directions for technological development can be presented regarding the correction of measurement errors induced by vapor in outdoor environments.

Associations of bedroom PM2.5, CO2, temperature, humidity, and noise with sleep: An observational actigraphy study

OBJECTIVE

Climate change and urbanization increasingly cause extreme conditions hazardous to health. The bedroom environment plays a key role for high-quality sleep. Studies objectively assessing multiple descriptors of the bedroom environment as well as sleep are scarce.

METHODS

Particulate matter with a particle size <2.5 µm (PM2.5), temperature, humidity, carbon dioxide (CO2), barometric pressure, and noise levels were continuously measured for 14 consecutive days in the bedroom of 62 participants (62.9% female, mean ± SD age: 47.7 ± 13.2 years) who wore a wrist actigraph and completed daily morning surveys and sleep logs.

RESULTS

In a hierarchical mixed effect model that included all environmental variables and adjusted for elapsed sleep time and multiple demographic and behavioral variables, sleep efficiency calculated for consecutive 1-hour periods decreased in a dose-dependent manner with increasing levels of PM2.5, temperature, CO2, and noise. Sleep efficiency in the highest exposure quintiles was 3.2% (PM2.5, p < .05), 3.4% (temperature, p < .05), 4.0% (CO2, p < .01), and 4.7% (noise, p < .0001) lower compared to the lowest exposure quintiles (all p-values adjusted for multiple testing). Barometric pressure and humidity were not associated with sleep efficiency. Bedroom humidity was associated with subjectively assessed sleepiness and poor sleep quality (both p < .05), but otherwise environmental variables were not statistically significantly associated with actigraphically assessed total sleep time and wake after sleep onset or with subjectively assessed sleep onset latency, sleep quality, and sleepiness. Assessments of bedroom comfort suggest subjective habituation irrespective of exposure levels.

CONCLUSIONS

These findings add to a growing body of evidence highlighting the importance of the bedroom environment-beyond the mattress-for high-quality sleep.

Substantial short- and long-term health effect due to PM2.5 and the constituents even under future emission reductions in China

Heavy pollution events of fine particulate matter (PM_2.5) frequently occur in China, seriously affecting the human health. However, how meteorological factors and anthropogenic emissions affect PM_2.5 and the major constituents, as well as the subsequent health effect, remains unclear. Here, based on regional climate and air quality models Weather Research and Forecasting (WRF) and Community Multiscale Air Quality (CMAQ), the PM_2.5 and major constituents in China at present and mid-century under the carbon neutral scenario Shared Socioeconomic Pathways (SSP)1-2.6 are simulated. Due to anthropogenic emission reduction, concentrations of PM_2.5 and the constituents decrease substantially in SSP1-2.6. The long-term exposure premature deaths at present are 2.23 million per year in mainland China, which is projected to increase by 76 % under SSP1-2.6 despite emission reduction, primarily attributable to aging which strikingly offsets the effect of air quality improvement. The number of annual premature deaths resulting from short-term exposure is 228,104 in mainland China at present, which is projected to decrease in the future. Using North China Plain as an example, we identify that among the major constituents of PM_2.5, organic carbon leads to the most short-term exposure deaths considering the largest exposure-response coefficient. Regarding the abnormally meteorological conditions, we find, relative to low relative humidity (RH) and non-stagnation, the compound events, defined as concurrence of high RH and atmospheric stagnation, exhibit an amplified role inducing larger premature deaths compared to the additive effect of the individual event of high RH and atmospheric stagnation. This nonlinear effect occurs at both present and future, but diminished in future due to emission reductions. Our study highlights the importance of considering both the long- and short-term premature deaths associated with PM_2.5 and the constituents, as well as the critical effect of extreme weather events.

Analysis of seasonal and spatial distribution of particulate matters and gaseous pollutants around an open cast coal mining area of Odisha, India

Open cast mining - a predominant method of coal production in India (94.46% of total coal production) - has been found to be a major factor which is responsible for the emission of dust particles and gaseous pollutants, leading to the deterioration of air quality in the coal mining area. Considering the health concerns and environmental impacts of these pollutants, the inhabited villages of Ib valley coalfield area of Orisha, India, were selected for this study. In this regard, various researchers have performed the analysis of air quality data and modeling for the dispersion of pollutants. However, a long-term study on spatial and seasonal variations of air pollutants and their relationship with meteorological parameters were missing in the literature. Accordingly, the spatial and seasonal variations of air pollutants in the area were assessed for a period of six years (2014 - 2020), and concentrations of PM_2.5, PM₁₀, and SPM were found to be above the annual national ambient air quality standards (NAAQS) for all the three seasons. The overall mean concentrations of NO_x, PM₁₀, PM_2.5, SPM, and SO₂ during this period were found to be 17.2 ± 9.28, 152.5 ± 99.7, 53.27 ± 37.70, 268.5 ± 158.2, and 12.58 ± 7.47 μg/m³, respectively. The analysis of meteorological parameters showed a strong and significant negative correlation of relative humidity with PM_2.5 (r =  - 0.30, p-value = 5.659 × 10^-10), PM₁₀ (r =  - 0.36, p-value = 1.97 × 10^-13), and SPM (r =  - 0.45, p-value = 2.2 × 10^-16). Furthermore, the spatial distribution of pollutants was performed using the geographic information system (GIS) and inverse distance weighting (IDW) method, wherein the seasonal distribution of pollutants was shown through the bivariate polar plots. Therefore, the analyses and recommendations provided in this study can help the policymakers in developing a long-term air quality improvement strategy around a coal mining area, including the spatial and seasonal variations of air pollutants and their relationship with meteorological parameters.

Analysis of Factors Influencing Air Quality in Different Periods during COVID-19: A Case Study of Tangshan, China

This study aimed to analyze the main factors influencing air quality in Tangshan during COVID-19, covering three different periods: the COVID-19 period, the Level I response period, and the Spring Festival period. Comparative analysis and the difference-in-differences (DID) method were used to explore differences in air quality between different stages of the epidemic and different years. During the COVID-19 period, the air quality index (AQI) and the concentrations of six conventional air pollutants (PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃-8h) decreased significantly compared to 2017-2019. For the Level I response period, the reduction in AQI caused by COVID-19 control measures were 29.07%, 31.43%, and 20.04% in February, March, and April of 2020, respectively. During the Spring Festival, the concentrations of the six pollutants were significantly higher than those in 2019 and 2021, which may be related to heavy pollution events caused by unfavorable meteorological conditions and regional transport. As for the further improvement in air quality, it is necessary to take strict measures to prevent and control air pollution while paying attention to meteorological factors.

Ambient relative humidity-dependent obstructive sleep apnea severity in cold season: A case-control study

BACKGROUND

The objective of this study was to examine associations of daily averages and daily variations in ambient relative humidity (RH), temperature, and PM_2.5 on the obstructive sleep apnea (OSA) severity.

METHODS

A case-control study was conducted to retrospectively recruit 8628 subjects in a sleep center between January 2015 and December 2021, including 1307 control (apnea-hypopnea index (AHI) < 5 events/h), 3661 mild-to-moderate OSA (AHI of 5-30 events/h), and 3597 severe OSA subjects (AHI > 30 events/h). A logistic regression was used to examine the odds ratio (OR) of outcome variables (daily mean or difference in RH, temperature, and PM_2.5 for 1, 7, and 30 days) with OSA severity (by the groups). Two-factor logistic regression models were conducted to examine the OR of RH with the daily mean or difference in temperature or PM_2.5 with OSA severity. An exposure-response relationship analysis was conducted to examine the outcome variables with OSA severity in all, cold and warm seasons.

RESULTS

We observed associations of mean PM_2.5 and RH with respective increases of 0.04-0.08 and 0.01-0.03 events/h for the AHI in OSA patients. An increase in the daily difference of 1 % RH increased the AHI by 0.02-0.03 events/h in OSA patients. A daily PM_2.5 decrease of 1 μg/m³ reduced the AHI by 0.03 events/h, whereas a daily decrease in the RH of 1 % reduced the AHI by 0.03-0.04 events/h. The two-factor model confirmed the most robust associations of ambient RH with AHI in OSA patients. The exposure-response relationship in temperature and RH showed obviously seasonal patterns with OSA severity.

CONCLUSION

Short-term ambient variations in RH and PM_2.5 were associated with changes in the AHI in OSA patients, especially RH in cold season. Reducing exposure to high ambient RH and PM_2.5 levels may have protective effects on the AHI in OSA patients.

Novel insights into indoor air purification capability of microalgae: characterization using multiple air quality parameters and comparison with common methods

Indoor air purification received more attention recently. In this study, the effects of six common indoor ornamental plants (Epripremnum aureum, Chlorphytum comosum, Aloe vera, Sedum sediforme, Cereus cv. Fairy Castle, and Sedum adolphii) and three kinds of microalgae (Chlorella sp. HQ, Scenedesmus sp. LX1, and C. vulgaris) on the removal of four types of air pollutants (particulate matters less than 2.5 (PM_2.5) and 10 μm (PM₁₀) in size, formaldehyde (HCHO) and total volatile organic compounds (VOC_S)) in test chamber compared with common physical purification methods (high efficiency particulate air filter and nano activated carbon absorption) were investigated. Their effects on oxygen, carbon dioxide, and relative humidity were also evaluated. The results showed that microalgae, especially C. vulgaris, was more suitable for removing PM_2.5 and PM₁₀, and the removal rates were 55.42 ± 25.77% and 45.76 ± 5.32%, respectively. The removal rates of HCHO and VOCs by all three kings of microalgae could reach 100%. Part of ornamental plants took a longer time to achieve 100% removal of HCHO and VOCs. Physical methods were weaker than ornamental plants and microalgae in terms of increased relative humidity and O₂ content. In general, microalgae, especially C. vulgaris could purify indoor air pollutants more efficiently. The above studies provided data and theoretical support for the purification of indoor air pollutants by microalgae.

Seasonal and vertical characteristics of particulate and elemental concentrations along diverse street canyons in South India

The impact of street geometries on vertical dispersion of PMs (PM_2.5 and PM₁₀) in (1) non-street canyon (NSC), (2) street canyon (SC), and (3) street canyon with viaduct (SCV) was studied during four seasons. The chemical composition of the species was analysed for source apportionment. The mass concentration of PMs in canyons was in the order of SCV > SC > NSC, implicating the canyon effect. Independent of height, most of the PM concentrations in SC and SCV violated the National Ambient Air Quality Standards (NAAQS) and exceeded the World Health Organization (WHO) guidelines in all three street geometries. The vertical concentration trend of PMs was significant during winter and summer seasons in NSC and SC. The vertical trend of both PMs was significant during summer and monsoon seasons in SCV. The seasonal change in PMs' vertical trend was influenced by atmospheric stability, wind velocities associated with street morphology, and emission sources. The ratio of PM_2.5/PM₁₀ indicated the dominance of PM₁₀ in all three locations. Among the estimated species, Fe (crustal and vehicle) and Na (sea salt and crustal) were abundant in PM_2.5 and PM₁₀, respectively. Estimation of enrichment factor (EF) revealed that most of the emission sources were anthropogenic in PM_2.5 and natural in PM₁₀. Principal component analysis (PCA) showed crustal/soil dust, vehicular emission, and sea salt to the common source profile for PMs. Specific contribution of smoking activity contributed to Be and Tl in PM_2.5, which may be considered a site-specific source.

Effect of PM2.5 Levels on ED Visits for Respiratory Causes in a Greek Semi-Urban Area

Fine particulate matter that have a diameter of <2.5 μm (PM2.5) are an important factor of anthropogenic pollution since they are associated with the development of acute respiratory illnesses. The aim of this prospective study is to examine the correlation between PM2.5 levels in the semi-urban city of Volos and Emergency Department (ED) visits for respiratory causes. ED visits from patients with asthma, pneumonia and upper respiratory infection (URI) were recorded during a one-year period. The 24 h PM2.5 pollution data were collected in a prospective manner by using twelve fully automated air quality monitoring stations. PM2.5 levels exceeded the daily limit during 48.6% of the study period, with the mean PM2.5 concentration being 30.03 ± 17.47 μg/m3. PM2.5 levels were significantly higher during winter. When PM2.5 levels were beyond the daily limit, there was a statistically significant increase in respiratory-related ED visits (1.77 vs. 2.22 visits per day; p: 0.018). PM2.5 levels were also statistically significantly related to the number of URI-related ED visits (0.71 vs. 0.99 visits/day; p = 0.01). The temperature was negatively correlated with ED visits (r: −0.21; p < 0.001) and age was found to be positively correlated with ED visits (r: 0.69; p < 0.001), while no statistically significant correlation was found concerning humidity (r: 0.03; p = 0.58). In conclusion, PM2.5 levels had a significant effect on ED visits for respiratory causes in the city of Volos.

Polyelectrolyte/Graphene Oxide Nano-Film Integrated Fiber-Optic Sensors for High-Sensitive and Rapid-Response Humidity Measurement

Optical fiber humidity sensors have sparked enormous interests in many fields because of their excellent features. However, it remains a great challenge to balance sensitivity, humidity response, temperature crosstalk, and wet hysteresis for real-world application. To overcome this trade-off, an optical fiber humidity sensor is developed here by coating functional graphene oxide (GO)/polyelectrolyte nanocomposite film on the excessively tilted fiber grating (ex-TFG), in which GO/polyelectrolyte nanocomposite film is employed for enhancing the hydrophilicity and accelerating the adsorption/desorption of water molecule, while the ex-TFG is utilized for improving the sensitivity of refractive index and eliminating the crosstalk of temperature. By this design, optical fiber humidity sensors achieve high sensitivity, rapid response and recovery, low hysteresis, and temperature crosstalk as well as excellent repeatability and stability in large relative humidity (RH) range. Our work provides a promising platform for effective RH monitoring systems that can be widely applied in rapid diagnostics, pharmacy, precision medicine, and so forth.

Interactive short-term effects of meteorological factors and air pollution on hospital admissions for cardiovascular diseases

A substantial number of studies have demonstrated the association between air pollution and adverse health effects. However, few studies have explored the potential interactive effects between meteorological factors and air pollution. This study attempted to evaluate the interactive effects between meteorological factors (temperature and relative humidity) and air pollution ([Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]) on cardiovascular diseases (CVDs). Next, the high-risk population susceptible to air pollution was identified. We collected daily counts of CVD hospitalizations, air pollution, and weather data in Nanning from January 1, 2014, to December 31, 2015. Generalized additive models (GAMs) with interaction terms were adopted to estimate the interactive effects of air pollution and meteorological factors on CVD after controlling for seasonality, day of the week, and public holidays. On low-temperature days, an increase of [Formula: see text] in [Formula: see text], [Formula: see text], and [Formula: see text] was associated with increases of 4.31% (2.39%, 6.26%) at lag 2; 2.74% (1.65%, 3.84%) at lag 0-2; and 0.13% (0.02%, 0.23%) at lag 0-3 in CVD hospitalizations, respectively. During low relative humidity days, a [Formula: see text] increment of lag 0-3 exposure was associated with increases of 3.43% (4.61%, 2.67%) and 0.10% (0.04%, 0.15%) for [Formula: see text] and [Formula: see text], respectively. On high relative humidity days, an increase of [Formula: see text] in [Formula: see text] was associated with an increase of 5.86% (1.82%, 10.07%) at lag 0-2 in CVD hospitalizations. Moreover, elderly (≥ 65 years) and female patients were vulnerable to the effects of air pollution. There were interactive effects between air pollutants and meteorological factors on CVD hospitalizations. The risk that [Formula: see text], [Formula: see text], and [Formula: see text] posed to CVD hospitalizations could be significantly enhanced by low temperatures. For [Formula: see text] and [Formula: see text], CVD hospitalization risk increased in low relative humidity. The effects of [Formula: see text] were enhanced at high relative humidity.

Levels, distributions and influential factors of residential airborne culturable bacteria in 12 Chinese cities: Multicenter on-site survey among dwellings

Residential airborne culturable bacteria (RAB) are commonly used to assess indoor microbial loads, which is a very effective and recognized indicator of public concern about residential air quality. Many countries and organizations have set exposure limits for residential bacteria. Nevertheless, few studies have been conducted in multicenter cities about the distribution and influencing factors of RAB. It is a challenge to investigate the distribution of RAB and identify the association between indoor influencing variables and RAB in China. The current finding implied the comparative results from a one-year on-site survey of 12 cities in China. The concentration of RAB ranged from 0 CFU/m³ to 18,078 CFU/m³, with an arithmetic median of 350 CFU/m³. RAB concentrations were more in the warm season than those in the cold season, and were more in the bedrooms than those in the living rooms. Indoor environmental indicators (including PM_2.5 and PM₁₀) showed the mediating role in the process of temperature and relative humidity effects on RAB. . Influential factors including family-related information (income), architectural characteristics (house type, building history, living floor, the layers of window glass, and decoration) and lifestyle behaviors (heating, new furniture, incense-burned, insecticides-used, air condition-used, and plants-growed) were related with the concentration of RAB. This study presents essential data on the distribution of RAB in some Chinese cities, and reveals the residential influential factors that might minimize health risk from RAB.

A PM2.5 concentration estimation method based on multi-feature combination of image patches

An efficient, accurate and high-resolution PM_2.5 monitoring approach is critical to pollution control and public health. Here we propose an image-based method for PM_2.5 concentration estimation. The method combines the image features with other influence factors to inference PM_2.5, and an improved patchwise strategy is used in the processes of regression and prediction. The experimental results of the Shanghai scene dataset show that our method achieved a higher estimation accuracy with 0.88 at R² and 10.42 μg⋅m^-3 at RMSE, compared to other methods; the addition of the influence factors, such as relative humidity and photographing month, improve the accuracy, while the improved patchwise strategy significantly enhanced the predictive performance. Moreover, the results of two datasets at different times and location further demonstrate the effectiveness and applicability of the proposed method.

Machine Learning for Determining Interactions between Air Pollutants and Environmental Parameters in Three Cities of Iran

Air pollution, as one of the most significant environmental challenges, has adversely affected the global economy, human health, and ecosystems. Consequently, comprehensive research is being conducted to provide solutions to air quality management. Recently, it has been demonstrated that environmental parameters, including temperature, relative humidity, wind speed, air pressure, and vegetation, interact with air pollutants, such as particulate matter (PM), NO2, SO2, O3, and CO, contributing to frameworks for forecasting air quality. The objective of the present study is to explore these interactions in three Iranian metropolises of Tehran, Tabriz, and Shiraz from 2015 to 2019 and develop a machine learning-based model to predict daily air pollution. Three distinct assessment criteria were used to assess the proposed XGBoost model, including R squared (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Preliminary results showed that although air pollutants were significantly associated with meteorological factors and vegetation, the formulated model had low accuracy in predicting (R2PM2.5 = 0.36, R2PM10 = 0.27, R2NO2 = 0.46, R2SO2 = 0.41, R2O3 = 0.52, and R2CO = 0.38). Accordingly, future studies should consider more variables, including emission data from manufactories and traffic, as well as sunlight and wind direction. It is also suggested that strategies be applied to minimize the lack of observational data by considering second-and third-order interactions between parameters, increasing the number of simultaneous air pollution and meteorological monitoring stations, as well as hybrid machine learning models based on proximal and satellite data.

The Impact of COVID-19 Control Measures on Air Quality in Guangdong Province

COVID-19 control measures had a significant social and economic impact in Guangdong Province, and provided a unique opportunity to assess the impact of human activities on air quality. Based on the monitoring data of PM2.5, PM10, NO2, and O3 concentrations from 101 air quality monitoring stations in Guangdong Province from October 2019 to April 2020, the PSCF (potential source contribution factor) analysis and LSTM (long short-term memory) neural network were applied to explore the impact of epidemic control measures on air quality in Guangdong Province. Results showed that during the lockdown, the average concentration of PM2.5, PM10, NO2, and O3 decreased by 37.84%, 51.56%, 58.82%, and 24.00%, respectively. The ranges of potential sources of pollutants were reduced, indicating that air quality in Guangdong Province improved significantly. The Pearl River Delta, characterized by a high population density, recorded the highest NO2 concentration values throughout the whole study period. Due to the lockdown, the areas with the highest concentrations of O3, PM2.5, and PM10 changed from the Pearl River Delta to the eastern and western Guangdong. Moreover, LSTM simulation results showed that the average concentration of PM2.5, PM10, NO2, and O3 decreased by 46.34%, 54.56%, 70.63%, and 26.76%, respectively, which was caused by human-made impacts. These findings reveal the remarkable impact of human activities on air quality and provide effective theoretical support for the prevention and control of air pollution in Guangdong Province.

Seasonal prediction of daily PM2.5 concentrations with interpretable machine learning: a case study of Beijing, China

Machine learning (ML) has shown high predictive ability in environmental research. Accurate estimation of daily PM_2.5 concentrations is a prerequisite to address environmental public health issues. However, studies on the interpretability of ML algorithms were limited. In this study, we aimed to estimate the daily concentrations of PM_2.5 at a seasonal level, and to understand the potential mechanisms of ML algorithms' decisions with SHapley Additive exPlanations (SHAP). Daily ground PM_2.5 concentrations and meteorological data were obtained from the Beijing Municipal Ecological and Environmental Monitoring Center, and China Meteorological Data Service Centre between December 2013 and 2019 November. We calculated correlation coefficient and variance inflation factor (VIF) to eliminate the variables with collinearity, and recursive feature elimination (RFE) was further used to selected more important predictors. A series of ML algorithms, including linear regression, the variants of linear regression (Ridge, Lasso, Elasticnet), decision tree (DT), k-nearest neighbor (KNN), support vector regression (SVR), ensemble methods (random forest: RF, eXtreme Gradient Boosting: XGBoost), and deep learning (long short-term memory network: LSTM), were developed to estimate seasonal-level daily PM_2.5 concentrations. A 10-fold cross validation was used to tune hyperparameters, and root mean square error (RMSE), mean absolute error (MAE), ratio of performance to deviation (RPD), and Lin's concordance correlation coefficient (LCCC) were used to evaluate models' performance. SHAP was performed for local and global interpretability analysis. The results showed that the distribution of PM_2.5 concentrations in Beijing showed obvious seasonal patterns. A total of five variables (Precipitation, Mean wind speed, Sunshine duration, Mean surface temperature, Mean relative humidity) were selected for final prediction. LSTM showed much higher accuracy than other traditional ML models, achieved the smallest RMSE of 19.58 µg/m³ and MAE of 15.11 µg/m³. In terms of selected data set, there was acceptable (LCCC = 0.41 ~ 0.52) agreement and accuracy (RPD = 0.97 ~ 1.92) for LSTM. The SHAP analyses revealed that the meteorological factors had different influences in specific predictions, and the complex interactions were also illustrated. These results enhance our understanding of meteorological factors-PM_2.5 relationships and explain the mechanisms of ML algorithms' decisions.

Short-term effect of meteorological factors on the risk of rheumatoid arthritis hospital admissions: A distributed lag non-linear analysis in Hefei, China

Rheumatoid arthritis (RA) is an autoimmune disease, mainly characterized by erosional arthritis. The proportion of adults suffering from RA is about 0.5%-1%. There have been reports on the association of rainfall and traffic-related air pollutants with RA hospitalization rates. However, there have been no studies on the association of diurnal temperature range (DTR) and relative humidity (RH) with RA hospitalization rates. This study aimed to examine the short-term association of DTR, RH and other meteorological factors with the hospital admission rate of RA patients, while excluding the interference of PM_2.5, SO₂, NO₂, CO and O₃ atmospheric pollutants. We collected daily RA occupancy rate and meteorological factor data in Hefei city from 2015 to 2018 and used the generalized additive model (GAM) combined with the distributed lag nonlinear model (DLNM) for time series analysis, and further stratified analysis by gender and age. Single-day and cumulative-day risk estimates of RA admissions were expressed as relative risk (RR) and its 95% confidence interval (95% CI). For the cumulative-day lag model, high RH was statistically significant after cumulative lag 0-8 days, and the effect gradually increases. Stratified analysis shows that females seem to be more susceptible to high or extremely high DTR and RH exposure, and extremely high DTR exposure may increase the risk of RA admission in all populations. In conclusion, this study found that high DTR and high RH exposure increased the risk of hospitalization in RA patients and provided clues to the potential association between other meteorological factors and RA.

Influence of number of visitors and weather conditions on airborne particulate matter mass concentrations at the Plitvice Lakes National Park, Croatia during summer and autumn

We investigated the influence of local meteorological conditions and number of visitors on ambient particulate matter (PM) mass concentrations and particle fraction ratios at the Plitvice Lakes National Park between July and October 2018. Outdoor mass concentrations of particles with aerodynamic diameters of less than 1, 2.5, and 10 μm (PM1, PM2.5, and PM10, respectively) and indoor PM1 were measured with two light-scattering laser photometers set up near the largest and most visited Kozjak Lake. Our findings suggest that the particles mainly originated from background sources, although some came from local anthropogenic activities. More specifically, increases in both indoor and outdoor mass concentrations coincided with the increase in the number of visitors. Indoor PM1 concentrations also increased with increase in outdoor air temperature, while outdoor PMs exhibited U-shaped dependence (i.e., concentrations increased only at higher outdoor air temperatures). This behaviour and the decrease in the PM1/PM2.5 ratio with higher temperatures suggests that the production and growth of particles is influenced by photochemical reactions. The obtained spectra also pointed to a daily but not to weekly periodicity of PM levels.

Particulate pollution over an urban Himalayan site: Temporal variability, impact of meteorology and potential source regions

Five-year (2013-2017) particulate matter (PM) data observed at an urban site, Srinagar, Kashmir Himalaya, India was used to examine the temporal variability, meteorological impacts and potential source regions of PM. The daily mean PM₁₀ and PM_2.5 concentration was 135 ± 112 μg/m³ and 87 ± 93 μg/m³ respectively with significant intra- and inter-daily variation. The annual PM₁₀ and PM_2.5 concentration was 2.0-3.2 and 1.7-2.8 times higher than the annual Indian National Ambient Air Quality Standards (PM₁₀ = 60 μg/m³ and PM_2.5 = 40 μg/m³). PM concentration shows a bimodal diurnal pattern with morning and evening peaks, which coincide with the increased anthropogenic activity and shallow planetary boundary layer (PBL). The combined effect of the low temperature, low wind speed, shallow and stable PBL and geomorphic setup of Kashmir valley leads to the accumulation of particulate pollution during autumn and winter and the converse meteorological conditions leads to dispersion, dilution and deposition during spring and summer. High precipitation rate (>15 mm/day) removes the coarse particles (PM₁₀) more efficiently than fine particles (PM_2.5), while as the moderate to high humid conditions (55-95%) leads to the accumulation and growth of more PM. It was observed that ~80% of the air masses arriving at the site during spring, autumn and winter are westerlies. Source contribution analysis revealed that highly potential source regions of PM at the site are neighboring Pakistan, Afghanistan, parts of Iran and Trans-Gangetic Plains, which could contribute high concentration of the PM₁₀ (>250 μg/m³) and PM_2.5 (>150 μg/m³) during autumn and winter. The high PM load observed at the site during autumn and winter, with major contribution from the anthropogenic source emissions like biomass and coal burning, fossil fuel combustion and suspension of road dust, is aggravated by the geomorphic and meteorological setup of the Kashmir valley.

Spatio-temporal modeling of PM2.5 risk mapping using three machine learning algorithms

Urban air pollution is one of the most critical issues that affect the environment, community health, economy, and management of urban areas. From a public health perspective, PM_2.5 is one of the primary air pollutants, especially in Tehran's metropolis. Owing to the different patterns of PM_2.5 in different seasons, Spatio-temporal modeling and identification of high-risk areas to reduce its effects seems necessary. The purpose of this study was Spatio-temporal modeling and preparation of PM_2.5 risk mapping using three machine learning algorithms (random forest (RF), AdaBoost, and stochastic gradient descent (SGD)) in the metropolis of Tehran, Iran. Therefore, in the first step, to prepare the dependent variable data, the PM_2.5 average was used for the four seasons of spring, summer, autumn, and winter. Then, using remote sensing (RS) and a geographic information system (GIS), independent data such as temperature, maximum temperature, minimum temperature, wind speed, rainfall, humidity, normalized difference vegetation index (NDVI), population density, street density, and distance to industrial centers were prepared as a seasonal average. To Spatio-temporal modeling using machine learning algorithms, 70% of the data were used for training and 30% for validation. The frequency ratio (FR) model was used as input to machine learning algorithms to calculate the spatial relationship between PM_2.5 and the effective parameters. Finally, Spatio-temporal modeling and PM_2.5 risk mapping were performed using three machine learning algorithms. The receiver operating characteristic (ROC) area under the curve (AUC) results showed that the RF algorithm had the greatest modeling accuracy, with values of 0.926, 0.94, 0.949, and 0.949 for spring, summer, autumn, and winter, respectively. According to the RF model, the most important variable in spring and autumn was NDVI. Temperature and distance to industrial centers were the most important variables in the summer and winter, respectively. The results showed that autumn, winter, summer, and spring had the highest risk of PM_2.5, respectively.

Development of season-dependent land use regression models to estimate BC and PM1 exposure

Reliable estimation of exposure to black carbon (BC) and sub-micrometer particles (PM₁) within a city is challenging because of limited monitoring data as well as the lack of models suitable for assessing the intra-urban environment. In this study, to estimate exposure levels in the inner-city area, we developed land use regression (LUR) models for BC and PM₁ based on specially designed mobile monitoring surveys conducted in 2019 and 2020 for three seasons. The daytime and nighttime LUR models were developed separately to capture additional details on the variation in pollutants. The results of mobile monitoring indicated similar temporal variation characteristics of BC and PM₁. The mean concentrations of pollutants were higher in winter (BC: 4.72 μg/m³; PM₁: 56.97 μg/m³) than in fall (BC: 3.74 μg/m³; PM₁: 33.29 μg/m³) and summer (BC: 2.77 μg/m³; PM₁: 27.04 μg/m³). For both BC and PM₁, higher nighttime concentrations were found in winter and fall, whereas higher daytime concentrations were observed in the summer. A supervised forward stepwise regression method was used to select the predictors for the LUR models. The adjusted R² of the LUR models for BC and PM₁ ranged from 0.39 to 0.66 and 0.45 to 0.80, respectively. Traffic-related predictors were incorporated into all the models for BC. In contrast, more meteorology-related predictors were incorporated into the PM₁ models. The concentration surface based on the LUR models was mapped at a spatial resolution of 100 m, and significant seasonal and diurnal trends were observed. PM₁ was dominated by seasonal variations, whereas BC showed more spatial variation. In conclusion, the development of season-dependent diurnal LUR models based on mobile monitoring could provide a methodology for the estimation of exposure and screening of influencing factors of BC and PM₁ in typical inner-city environments, and support pollution management.

Particulate Matter Removal of Three Woody Plant Species, Ardisia crenata, Ardisia japonica, and Maesa japonica

In this study, we investigated the physiological responses and particulate matter (PM) abatement and adsorption of three plants: Ardisia crenata, Ardisia japonica, and Maesa japonica, to determine their effectiveness as indoor air purification. When compared to control (without plants), PM was significantly and rapidly decreased by all three plants. The reduction in PM varied by species, with A. crenata being the most effective, followed closely by A. japonica, and finally M. japonica. M. japonica showed the highest rate of photosynthesis and transpiration, generating the greatest decrease in CO2 and a large increase in relative humidity. We hypothesize that the increased relative humidity in the chamber acted in a manner similar to a chemical flocculant, increasing the weight of PM via combination with airborne water particles and the creation of larger PM aggregates, resulting in a faster sedimentation rate. A. crenata had a stomatal size of ~20 μm or larger, suggesting that the PM reduction observed in this species was the result of direct absorption. In the continuous fine dust exposure experiments, chlorophyll fluorescence values of all three species were in the normal range. In conclusion, all three species were found to be suitable indoor landscaping plants, effective at reducing indoor PM.

Effects of COVID-19 pandemic on the air quality of three megacities in India

COVID-19 pandemic compelled many countries in the world to go for a nationwide lockdown to prevent the spread of the coronavirus. India started the lockdown on 24 March 2020. We analyzed the air quality of three megacities of India, namely Mumbai, Delhi, and Kolkata, during the lockdown phase and compared it with the pre-lockdown and post-lockdown scenarios. We considered seven major air pollutants: PM_2.5, PM₁₀, NO₂, NH₃, SO₂, CO, and O₃. We analyzed the data acquired from 56 automatic air-monitoring stations (AAMS) under the Central Pollution Control Board (CPCB) spread across the megacities. The air pollution level in the eastern part of Mumbai and the western part of Delhi and Kolkata usually remains high. Delhi was the worst polluted megacity, followed by Kolkata and Mumbai. The stop of vehicular movements and industrial lockdown across the nation has substantial effects on the environment, especially in the atmosphere near the Earth's surface. Our analysis showed significant improvements in air quality during the period of lockdown (25 March to 14 April 2020) compared to the pre-lockdown phase (3 March to 23 March 2020) and the same time window of the previous year (25 March to 14 April 2019). The post-lockdown (15 April to 5 May) phase exhibited mixed results. We mapped the spatial pattern of these pollutants and the air quality index (AQI). According to CPCB, PM_2.5, PM₁₀, and CO are the major air pollutants in India that reduced by 47%, 41%, and 27% in Mumbai; 52%, 39%, and 13% in Delhi; and 49%, 37%, and 21% in Kolkata, respectively, in the lockdown phase. PM_2.5, PM₁₀, and NO₂ exhibited significant correlations across the three megacities. This study shows that occasional short-term lockdowns can effectively refresh the air in these megacities.

Indoor air quality investigation of a badminton hall in humid season through objective and subjective approaches

This study investigated the indoor air quality (IAQ) during humid season in an old badminton hall, to explore the IAQ characteristics of natural ventilated sports buildings for public use. The indoor air parameters (temperature, relative humidity and air velocity) and indoor air pollutants (CO₂, TVOC, PM_2.5 and PM₁₀) were measured. A subjective approach was carried out through questionnaire survey. 185 valid questionnaires were recovered, and 68.7% of the participants had exercised. Results show that the indoor air qualities obtained through objective and subjective approaches were obviously different. Indoor PM, TVOC and CO₂ concentrations were normal, but 37.3% of the participants complained about the building materials' smell and 73.5% of the participants reported obvious sweaty odor. Physical activity might reduce a person's sensitivity to the environment. The participants generally felt warm and hot because of the high relative humidity. Post-exercise participants felt significantly hotter than those who did not exercise, and were generally more receptive to IAQ. The method of Fanger was employed to narrow the gap between subjective and objective approaches with a modified parameter, and to furtherly estimate the ventilation. The present study demonstrates the necessity to combine two approaches together to assess the IAQ in sports buildings.

PM2.5 Pollution Strongly Predicted COVID-19 Incidence in Four High-Polluted Urbanized Italian Cities during the Pre-Lockdown and Lockdown Periods

BACKGROUND

The coronavirus disease in 2019 (COVID-19) heavily hit Italy, one of Europe's most polluted countries. The extent to which PM pollution contributed to COVID-19 diffusion is needing further clarification. We aimed to investigate the particular matter (PM) pollution and its correlation with COVID-19 incidence across four Italian cities: Milan, Rome, Naples, and Salerno, during the pre-lockdown and lockdown periods.

METHODS

We performed a comparative analysis followed by correlation and regression analyses of the daily average PM10, PM2.5 concentrations, and COVID-19 incidence across four cities from 1 January 2020 to 8 April 2020, adjusting for several factors, taking a two-week time lag into account.

RESULTS

Milan had significantly higher average daily PM10 and PM2.5 levels than Rome, Naples, and Salerno. Rome, Naples, and Salerno maintained safe PM10 levels. The daily PM2.5 levels exceeded the legislative standards in all cities during the entire period. PM2.5 pollution was related to COVID-19 incidence. The PM2.5 levels and sampling rate were strong predictors of COVID-19 incidence during the pre-lockdown period. The PM2.5 levels, population's age, and density strongly predicted COVID-19 incidence during lockdown.

CONCLUSIONS

Italy serves as a noteworthy paradigm illustrating that PM2.5 pollution impacts COVID-19 spread. Even in lockdown, PM2.5 levels negatively impacted COVID-19 incidence.

Non-Combustible Source Indoor Air Pollutants Concentration in Beauty Salons and Associated Self-Reported Health Problems Among the Beauty Salon Workers

Background

Cosmetic products emits Total Volatile Organic Compound (TVOC) and Particulate Matter with an aerodynamic diameter of 10 micrometers (PM₁₀) of different sizes and characteristics with adverse health effects. Despite the increasing need for cosmetic products, related pollutants level of concentration from beauty salon is not well understood in developing countries.

Objective

This study aims to assess indoor air pollutant concentrations in the beauty salon and self-reported health problems among the salon workers in Jimma town.

Methods

A cross-sectional study design was used on 87 beauty salons from May 13-24, 2019. The concentrations of PM_10, TVOCs, CO₂, room temperature, and relative humidity were measured and triangulated with the survey data collected through measurements and questionnaires. A statistical software package, SPSS v.21, was used to analyze the data. A binary logistic regression was used to analyze categorical data and linear regressions to predict pollutants level and associated health outcomes.

Results

The results show that 93.1% of the respondents are females, and 85% were below 30 years old. More than 60% of the respondents were married individuals. 56.3% and 44.8% of the workers work over 10 hours per day and work the whole week. 34.6% of the workers reported as worked during pregnancy. About 70% of the workers know the harmful effects of cosmetics, benefits of ventilation, and Personal Protective Equipment (PPE) use, but only 19.4% use face masks. The majority (88.5%) reported health problems after starting work in the beauty salon. The mean volume of the beauty salon was 36.3 m^3, with a mean PM₁₀ concentration of 0.465 mg/m³ and a mean TVOC concentration of 1034.2 µg/m³. These air pollutants have shown a statistically significant association with self-reported health problems. Hence, urgent intervention with subsequent continuous awareness creation is needed to reduce the health consequences of a beauty salon's indoor air pollutants.

Impact of COVID-19 lockdown on air quality in Chandigarh, India: Understanding the emission sources during controlled anthropogenic activities

The variation in ambient air quality during COVID-19 lockdown was studied in Chandigarh, located in the Indo-Gangetic plain of India. Total 14 air pollutants, including particulate matter (PM₁₀, PM_2.5), trace gases (NO₂, NO, NO_x, SO₂, O₃, NH₃, CO) and VOC's (benzene, toluene, o-xylene, m,p-xylene, ethylbenzene) were examined along with meteorological parameters. The study duration was divided into four parts, i.e., a) 21 days of before lockdown b) 21 days of the first phase of lockdown c) 19 days of the second phase of lockdown d) 14 days of the third phase of lockdown. The results showed significant reductions during the first and second phases for all pollutants. However, concentrations increased during the third phase. The concentrations of SO₂, O_3, and m,p-xylene kept on increasing throughout the study period, except for benzene, which continuously decreased. The percentage decrease in the concentrations during consecutive periods of lockdown were 28.8%, 23.4% and 1.1% for PM_2.5 and 36.8%, 22.8% and 2.4% for PM₁₀ respectively. The Principal Component Analysis (PCA) and characteristic ratios identified vehicular pollution as a primary source during different phases of lockdown. During the lockdown, residential sources showed a significant adverse impact on the air quality of the city. Regional atmospheric transfer of pollutants from coal-burning and stubble burning were identified as secondary sources of air pollution. The findings of the study offer the potential to plan air pollution reduction strategies in the extreme pollution episodes such as during crop residue burning period over Indo-Gangetic plain.

The Influence of South East Asia Forest Fires on Ambient Particulate Matter Concentrations in Singapore: An Ecological Study Using Random Forest and Vector Autoregressive Models

Haze, due to biomass burning, is a recurring problem in Southeast Asia (SEA). Exposure to atmospheric particulate matter (PM) remains an important public health concern. In this paper, we examined the long-term seasonality of PM_2.5 and PM₁₀ in Singapore. To study the association between forest fires in SEA and air quality in Singapore, we built two machine learning models, including the random forest (RF) model and the vector autoregressive (VAR) model, using a benchmark air quality dataset containing daily PM_2.5 and PM₁₀ from 2009 to 2018. Furthermore, we incorporated weather parameters as independent variables. We observed two annual peaks, one in the middle of the year and one at the end of the year for both PM_2.5 and PM₁₀. Singapore was more affected by fires from Kalimantan compared to fires from other SEA countries. VAR models performed better than RF with Mean Absolute Percentage Error (MAPE) values being 0.8% and 6.1% lower for PM_2.5 and PM_10, respectively. The situation in Singapore can be reasonably anticipated with predictive models that incorporate information on forest fires and weather variations. Public communication of anticipated air quality at the national level benefits those at higher risk of experiencing poorer health due to poorer air quality.

Land use regression modelling of PM2.5 spatial variations in different seasons in urban areas

As one of the principal components of haze, fine particulate matter (PM_2.5) has potential negative health effects, causing widespread concern. Identification of the pollutant spatial variation is a prerequisite of understanding ambient air pollution exposure and further improving air quality. Seven urban built-up areas in Liaoning central urban agglomeration (LCUA) were used for land use regression (LUR) modelling of PM_2.5 concentrations using small amounts of spatially aggregated data and to assess the model's seasonal consistency. LUR models explained 52-61% of the variation in the PM_2.5 concentrations at urban scales. The average building floor area was the key predictor in each model, and the percent water area was predictor with a negative coefficient. Good seasonal consistency was observed between the heating-seasonal model and annual average model, showing that the annual average PM_2.5 pollution in the LCUA was mainly influenced by pollution during the heating season. Extending the linear LUR model with regression kriging improved the model's explanatory ability and predictive performance. The predicted PM_2.5 concentrations in Shenyang and Anshan were the highest and that in Yingkou was the lowest. The building three-dimensional variables played important roles in the urban spatial modelling of air pollution.

Household air pollution from cooking and heating and its impacts on blood pressure in residents living in rural cave dwellings in Loess Plateau of China

Cave dwelling is an ancient and unique type of residence in the Loess Plateau of Northern China, where the economics are less-developed. The majority of the local dwellers rely on traditional solid fuels for cooking and heating, which can emit large amounts of particles into both indoor and outdoor environments. In this study, we measured the real-time household concentrations of PM_2.5 and explored the association between personal daily PM_2.5 exposure and blood pressure (BP). Cooking and heating activities with different energies made a great variation in the household PM_2.5 air pollution, and residents using biomass had the highest personal PM_2.5 exposure. Temperature and relative humidity are both significantly linear correlated with household PM_2.5 air pollution. Besides, systolic blood pressure (SBP) was demonstrated to be positively associated with personal PM_2.5 exposure: with each 10-μg/m³ incremental PM_2.5 concentration when controlling all the other factors, SBP will increase by 0.36 mmHg (95% confident interval (CI) 0.05-0.0.77 mmHg). If solid fuels could be replaced with clean energies, personal PM_2.5 exposure and SBP would reduce by more than 21% and 3.7%, respectively, calling for efficient intervention programs to mitigate household air pollution of cave dwellings and protect health of those residents.

The spatially heterogeneous response of aerosol properties to anthropogenic activities and meteorology changes in China during 1980-2018 based on the singular value decomposition method

The unsustainable and rapid economy development brings air pollution prominently in China. In the last decade, the haze weather and its influencing mechanism across China have received increasingly attention. Although previous research has extensively focused on the characteristics of aerosols, better understanding of long-term variation in aerosols and their determinants since the Reform and Opening-up still lack in China. Furthermore, the previous studies exploring the influencing mechanism behind haze episodes by using statistical method only reflect correlation between pollutant concentration and indicators at single station, which cannot consider the remote influences resulting from atmosphere transport. In this research, we investigated the spatiotemporal pattern of aerosol optical depth (AOD) and aerosol species in China during 1980-2018 and explored the spatially heterogeneous response of AOD and aerosol component to meteorological conditions and urbanization based on singular value decomposition (SVD) method. The results indicated that AOD exhibited an upward trend in nearly 40 years, especially in eastern China with the fastest growth of sulfate aerosol. The heterogeneity of determinants revealed a great gap in anthropogenic activities and meteorological influences on aerosol varing regions. In eastern China, anthropogenic activities should be closely monitored. Besides, scientific desert governance and urban construction exert positive impact on air pollution in Xinjiang province.

Estimativa da concentração média diária de material particulado fino na região do Complexo Industrial e Portuário do Pecém, Ceará, Brasil

A exposição ao material particulado fino (MP2,5) está associada a inúmeros desfechos à saúde. Desta forma, monitoramento da concentração ambiental do MP2,5 é importante, especialmente em áreas amplamente industrializadas, pois abrigam potenciais emissores do MP2,5 e de substâncias com potencial de aumentar a toxicidade de partículas já suspensas. O objetivo desta pesquisa é estimar a concentração diária do MP2,5 em três áreas de influência do Complexo Industrial e Portuário do Pecém (CIPP), Ceará, Brasil. Foi aplicado um modelo de regressão não linear para a estimativa do MP2,5, por meio de dados de profundidade óptica monitorados por satélite. As estimativas foram realizadas em três áreas de influência (Ai) do CIPP (São Gonçalo do Amarante - Ai I, Paracuru e Paraipaba - Ai II e Caucaia - Ai III, no período de 2006 a 2017. As médias anuais das concentrações estimadas foram inferiores ao estabelecido pela legislação nacional em todas as Ai (8µg m-3). Em todas as Ai, os meses referentes ao período de seca (setembro a fevereiro) apresentaram as maiores concentrações e uma predominância de ventos leste para oeste. Os meses que compreendem o período de chuva (março a agosto) apresentaram as menores concentrações e ventos menos definidos. As condições meteorológicas podem exercer um papel importante nos processos de remoção, dispersão ou manutenção das concentrações do material particulado na região. Mesmo com baixas concentrações estimadas, é importante avaliar a constituição das partículas finas dessa região, bem como sua possível associação a efeitos adversos à saúde da população local.

Exploring the spatiotemporal pattern of PM2.5 distribution and its determinants in Chinese cities based on a multilevel analysis approach

China has been under threat of severe haze in recent years, particularly that caused by fine particulate matter (PM_2.5). Exploring the determinants of PM_2.5 concentration is critical for improving air quality. The influencing mechanism of smog pollution is a comprehensive and systematic process affected by multiple driving factors. In this research, we collected PM_2.5 monitoring data from 292 cities across China in 2015 and employed multilevel regression models constructed using three levels to detect the physical and socioeconomic driving forces behind the PM_2.5 concentration at monthly, seasonal and spatial scales, which captured random effects both varied by season and region. The results indicated significant spatiotemporal heterogeneity in the PM_2.5 distribution, with the pollution core located in central China and northern China. The most severely haze episodes occurred in winter. Multilevel models showed that 46.40% of the variance was derived from the seasonal and spatial levels, and the models could explain a maximum of 90.7% of the PM_2.5 concentration variance. The multilevel model identified more determinant influences varying by time and region. The outcomes suggested that the impacts of temperature and relative humidity on PM_2.5 were of significant spatiotemporal heterogeneity due to the influencing mechanism differing from season and station. The variation of anthropogenic activities led to the socioeconomic influences featured a significant spatiotemporal heterogeneity. This research revealed the spatiotemporal characteristic of PM_2.5 pollution influencing mechanism from physical and perspective and provided effective strategies for restricting air pollution.

Factors controlling the long-term (2009-2015) trend of PM2.5 and black carbon aerosols at eastern Himalaya, India

A first-ever long-term (2009-2015) study on the fine particulate matter (PM_2.5) and black carbon (BC) aerosol were conducted over Himalaya in order to investigate the characteristics, temporal variations and the important factors regulating the long-term trend. The study was conducted over a high altitude station, Darjeeling (27°01'N, 88°15'E, 2200 m asl) representing a typical high altitude urban atmosphere at eastern Himalaya in India. The average concentrations of PM_2.5 and BC over a period of seven years were 25.2 ± 5.6 μg m^-3 (ranging between 2.2 and 220.4 μg m^-3) and 3.4 ± 0.7 μg m^-3 (0.4 to 15.6 μg m^-3) respectively. We observed decreasing trends in both PM_2.5 (49% at a rate of 170 ng m^-3 month^-1) and BC (34% at the rate of 20 ng m^-3 month^-1) mass concentration over this region from 2009 to 2015. We extensively studied the impact of micrometeorological parameters on the long-term trend in PM_2.5 and BC through the correlation analysis. The significant changes in boundary layer dynamics over this region played a major role in the decreasing trend of aerosols. The concentration weighted trajectory analysis revealed that the important contributory long-distant source regions for PM_2.5 and BC over eastern Himalaya were Indo Gangetic Plane and Nepal. The contributions from these regions were found to be decreased significantly from 2009 to 2015. Investigations on the fire counts associated with the forest fire, and open burning activities through the satellite observations revealed that the decreasing trend in PM_2.5 and BC over eastern Himalaya is well correlated to the decreasing trend in the fire counts over IGP and Nepal. We also explored that the changes and up gradation of the domestic fuel at the Indo Gangetic Plane regions in recent years not only improved the regional air quality but also affected the atmospheric environment over the eastern part of Himalaya.

Air stagnation and its impact on air quality during winter in Sichuan and Chongqing, southwestern China

The Sichuan and Chongqing regions suffer from severe haze weather in winter due to the unfavourable atmospheric diffusion conditions. Reanalysis and precipitation datasets were applied in this study to calculate and distinguish air stagnation events using a developed criterion, and the impacts of the occurrence of air stagnation events on air quality were analysed in combination with the PM_2.5 concentration data for the winters of 2013-2016. The highest occurrence frequency of air stagnation events was observed in 2013, and the lowest, 2015. The meteorological conditions during winter in the Sichuan Basin were inclined to form unfavourable atmospheric diffusion conditions, and the occurrence frequency of air stagnation days was up to 76.6% on average during the four winters. The effects of air stagnation events on air quality were most obvious in the western and southern Sichuan Basin. The mean concentrations of PM_2.5 during air stagnation days were higher by 41.9% than those during non-air stagnation days. The PM_2.5 concentrations were adjusted using the favourable atmospheric diffusion conditions in 2015 as a baseline to quantify the PM_2.5 contribution to the improvement of air quality in the other years, which revealed that the level of PM_2.5 in the Sichuan and Chongqing regions was declining at a rate of approximately 10.7% overall during the winters of 2013-2016, implying that the air pollutant reduction measures have been highly effective. Furthermore, the occurrence frequency of air stagnation days and events were increased in recent ten years of 2007-2016, with linear slopes of 0.61yr^-1 and 0.26yr^-1, respectively. The study revealed that the government might face a greater challenge in improving the air quality over winter and should pay more attention to reduction of pollutant emission in areas of Chengdu, Chongqing and cities in the south of the Sichuan Basin.