Rapid reduction of air pollution and short-term exposure risks in China.

With the continuous control of anthropogenic emissions, China's air quality has improved significantly in recent years. Given this background, research on how the short-term exposure risks caused by air pollution in China have changed is insufficient. This study utilized hourly concentration data from ground observation stations and the official air quality guidelines of the Ministry of Ecology and Environment of China and the World Health Organization as standards to systematically investigate the spatiotemporal characteristics and short-term exposure risks of air pollution in China from 2015 to 2022. The results indicate that various atmospheric pollutants except for ozone showed a decreasing trend yearly. Nationwide, both single pollutant air pollution days (SAPDs) and multiple pollutant air pollution days (MAPDs) showed varying degrees of reduction within 15 and 25 days, respectively. SAPD was dominated mainly by excessive PM2.5 and PM10 pollutants, while MAPD was dominated mainly by excessive pollutant combinations, including PM2.5 + PM10, CO + PM2.5 + PM10, and SO2 + PM2.5 + PM10. As the concentration of atmospheric pollutants decreased, the total excess risk (ER) decreased yearly from 2015 to 2022, but there were significant regional differences. Now, the ER is less than 0.25% in southern China, in the range of 0.25%-0.5% in the North China Plain and some cities in the northeast, and higher than 1% in the northwest. Particulate matter is currently the primary pollutant posing short-term exposure risk in China, especially due to the impact of sandstorm weather. This study indicates that China's atmospheric cleaning action is significantly beneficial for reducing health risks.

A water probe for direct pH measurement of individual particles via micro-Raman spectroscopy.

The acidity of atmospheric aerosols influences fundamental physicochemical processes that affect climate and human health. We recently developed a novel and facile water-probe-based method for directly measuring of the pH for micrometer-size droplets, providing a promising technique to better understand aerosol acidity in the atmosphere. The complex chemical composition of fine particles in the ambient air, however, poses certain challenges to using a water-probe for pH measurement, including interference from interactions between compositions and the influence of similar compositions on water structure. To explore the universality of our method, it was employed to measure the pH of ammonium, nitrate, carbonate, sulfate, and chloride particles. The pH of particles covering a broad range (0-14) were accurately determined, thereby demonstrating that our method can be generally applied, even to alkaline particles. Furthermore, a standard spectral library was developed by integrating the standard spectra of common hydrated ions extracted through the water-probe. The library can be employed to identify particle composition and overcome the spectral overlap problem resulting from similar effects. Using the spectral library, all ions were identified and their concentrations were determined, in turn allowing successful pH measurement of multicomponent (ammonium-sulfate-nitrate-chloride) particles. Insights into the synergistic effect of Cl-, NO3-, and NH4+ depletion obtained with our approach revealed the interplay between pH and volatile partitioning. Given the ubiquity of component partitioning and pH variation in particles, the water probe may provide a new perspective on the underlying mechanisms of aerosol aging and aerosol-cloud interaction.

Changes in source contributions to the oxidative potential of PM2.5 in urban Xiamen, China.

The toxicity of PM2.5 does not necessarily change synchronously with its mass concentration. In this study, the chemical composition (carbonaceous species, water-soluble ions, and metals) and oxidative potential (dithiothreitol assay, DTT) of PM2.5 were investigated in 2017/2018 and 2022 in Xiamen, China. The decrease rate of volume-normalized DTT (DTTv) (38%) was lower than that of PM2.5 (55%) between the two sampling periods. However, the mass-normalized DTT (DTTm) increased by 44%. Clear seasonal patterns with higher levels in winter were found for PM2.5, most chemical constituents and DTTv but not for DTTm. The large decrease in DTT activity (84%-92%) after the addition of EDTA suggested that water-soluble metals were the main contributors to DTT in Xiamen. The increased gap between the reconstructed and measured DTTv and the stronger correlations between the reconstructed/measured DTT ratio and carbonaceous species in 2022 were observed. The decrease rates of the hazard index (32.5%) and lifetime cancer risk (9.1%) differed from those of PM2.5 and DTTv due to their different main contributors. The PMF-MLR model showed that the contributions (nmol/(min·m3)) of vehicle emission, coal + biomass burning, ship emission and secondary aerosol to DTTv in 2022 decreased by 63.0%, 65.2%, 66.5%, and 22.2%, respectively, compared to those in 2017/2018, which was consistent with the emission reduction of vehicle exhaust and coal consumption, the adoption of low-sulfur fuel oil used on board ships and the reduced production of WSOC. However, the contributions of dust + sea salt and industrial emission increased.

Insights into the seasonal characteristics of single particle aerosols in Chengdu based on SPAMS.

To investigate the seasonal characteristics in air pollution in Chengdu, a single particle aerosol mass spectrometry was used to continuously observe atmospheric fine particulate matter during one-month periods in summer and winter, respectively. The results showed that, apart from O3, the concentrations of other pollutants (CO, NO2, SO2, PM2.5 and PM10) were significantly higher in winter than in summer. All single particle aerosols were divided into seven categories: biomass burning (BB), coal combustion (CC), Dust, vehicle emission (VE), K mixed with nitrate (K-NO3), K mixed with sulfate and nitrate (K-SN), and K mixed with sulfate (K-SO4) particles. The highest contributions in both seasons were VE particles (24%). The higher contributions of K-SO4 (16%) and K-NO3 (10%) particles occurred in summer and winter, respectively, as a result of their different formation mechanisms. S-containing (K-SO4 and K-SN), VE, and BB particles caused the evolution of pollution in both seasons, and they can be considered as targets for future pollution reduction. The mixing of primary sources particles (VE, Dust, CC, and BB) with secondary components was stronger in winter than in summer. In summer, as pollution worsens, the mixing of primary sources particles with 62 [NO3]- weakened, but the mixing with 97 [HSO4]- increased. However, in winter, the mixing state of particles did not exhibit an obvious evolution rules. The potential source areas in summer were mainly distributed in the southern region of Sichuan, while in winter, besides the southern region, the contribution of the western region cannot be ignored.

A comparative study on the formation of nitrogen-containing organic compounds in cloud droplets and aerosol particles.

Nitrogen-containing organic compounds (NOCs) may potentially contribute to aqueous secondary organic aerosols, yet the different formation of NOCs in aerosol particles and cloud droplets remains unclear. With the in-situ measurements performed at a mountain site (1690 m a.s.l.) in southern China, we investigated the formation of NOCs in the cloud droplets and the cloud-free particles, based on their mixing state information of NOCs-containing particles by single particle mass spectrometry. The relative abundance of NOCs in the cloud-free particles was significantly higher than those in cloud residual (cloud RES) particles. NOCs were highly correlated with carbonyl compounds (including glyoxalate and methylglyoxal) in the cloud-free particles, however, limited correlation was observed for cloud RES particles. Analysis of their mixing state and temporal variations highlights that NOCs was mainly formed from the carbonyl compounds and ammonium in the cloud-free particles, rather than in the cloud RES particles. The results support that the formation of NOCs from carbonyl compounds is facilitated in concentrated solutions in wet aerosols, rather than cloud droplets. In addition, we have identified the transport of biomass burning particles that facilitate the formation of NOCs, and that the observed NOCs is most likely contributed to the light absorption. These findings have implications for the evaluation of NOCs formation and their contribution to light absorption.

Estimation of gaseous polycyclic aromatic hydrocarbons (PAHs) and characteristics of atmospheric PAHs at a traffic site in Kanazawa, Japan.

Size-fractionated particulate matter (PM2.5 and PM>2.5) was collected at a traffic site in Kanazawa, Japan in a seasonal sampling work in 2020. Nine polycyclic aromatic hydrocarbons (4- to 6-ring PAHs) were determined in fine and coarse particles. The gas/particle partitioning coefficients (Kp) of the PAHs were calculated from the supercooled liquid vapour pressure and octanol-air partitioning coefficient based on the relationships obtained in previous traffic pollution-related studies. Gaseous PAHs were estimated by Kp and the concentrations of PM and particulate PAHs. The concentrations of total PAHs were 32.5, 320.1 and 5646.2 pg/m3 in the PM>2.5, PM2.5 and gas phases, respectively. Significant seasonal trends in PAHs were observed (particle phase: lowest in summer, gas phase: lowest in spring, particle and gas phase: lowest in spring). Compared to 2019, the total PAH concentrations (in particles) decreased in 2020, especially in spring and summer, which might be due to reduced traffic trips during the COVID-19 outbreak. The incremental lifetime cancer risk (ILCR) calculated from the toxic equivalent concentrations relative to benzo[a]pyrene (BaPeq) was lower than the acceptable limit issued by the US Environmental Protection Agency, indicating a low cancer risk in long-term exposure to current PAH levels. It is notable that gaseous PAHs considerably contributed to BaPeq and ILCR (over 50%), which highlighted the significance of gaseous PAH monitoring for public health protection. This low-cost estimation method for gaseous PAHs can be expected to reliably and conveniently obtain PAH concentrations as a surrogate for traditional sampling in the future work.

Airborne fungal communities are more susceptible to anthropogenic activities than bacteria.

Airborne microorganisms (AM) have significant environmental and health implications. Extensive studies have been conducted to investigate the factors influencing the composition and diversity of AM. However, the knowledge of AM with anthropogenic activities has not reach a consensus. In this study, we took advantage of the dramatic decline of outdoor anthropogenic activities resulting from COVID-19 lockdown to reveal their associations. We collected airborne particulate matter before and during the lockdown period in two cities. The results showed that it was fungal diversity and communities but not bacteria obviously different between pre-lockdown and lockdown samples, suggesting that airborne fungi were more susceptible to anthropogenic activities than bacteria. However, after the implementation of lockdown, the co-occurrence networks of both bacterial and fungal community became more complex, which might be due to the variation of microbial sources. Furthermore, Mantel test and correlation analysis showed that air pollutants also partly contributed to microbial alterations. Airborne fungal community was more affected by air pollutants than bacterial community. Notably, some human pathogens like Nigrospora and Arthrinium were negatively correlated with air pollutants. Overall, our study highlighted the more impacts of anthropogenic activities on airborne fungal community than bacterial community and advanced the understanding of associations between anthropogenic activities and AM.

Removal of particulate matter and dissolved organic matter from sedimentation sludge water during pre-sedimentation process: Performances and mechanisms.

Sedimentation sludge water (SSW), a prominent constituent of wastewater from drinking water treatment plants, has received limited attention in terms of its treatment and utilization likely due to the perceived difficulties associated with managing SSW sludge. This study comprehensively evaluated the water quality of SSW by comparing it to a well-documented wastewater (filter backwash water (FBW)). Furthermore, it investigated the pollutant variations in the SSW during pre-sedimentation process, probed the underlying reaction mechanism, and explored the feasibility of employing a pilot-scale coagulation-sedimentation process for SSW treatment. The levels of most water quality parameters were generally comparable between SSW and FBW. During the pre-sedimentation of SSW, significant removal of turbidity, bacterial counts, and dissolved organic matter (DOM) was observed. The characterization of DOM components, molecular weight distributions, and optical properties revealed that the macromolecular proteinaceous biopolymers and humic acids were preferentially removed. The characterization of particulates indicated that high surface energy, zeta potential, and bridging/adsorption/sedimentation/coagulation capacities in aluminum residuals of SSW, underscoring its potential as a coagulant and promoting the generation and sedimentation of inorganic-organic complexes. The coagulation-sedimentation process could effectively remove pollutants from low-turbidity SSW ([turbidity]0 < 15 NTU). These findings provide valuable insights into the water quality dynamics of SSW during the pre-sedimentation process, facilitating the development of SSW quality management and enhancing its reuse rate.

On using an aerosol thermodynamic model to calculate aerosol acidity of coarse particles.

Thermodynamic modeling is still the most widely used method to characterize aerosol acidity, a critical physicochemical property of atmospheric aerosols. However, it remains unclear whether gas-aerosol partitioning should be incorporated when thermodynamic models are employed to estimate the acidity of coarse particles. In this work, field measurements were conducted at a coastal city in northern China across three seasons, and covered wide ranges of temperature, relative humidity and NH3 concentrations. We examined the performance of different modes of ISORROPIA-II (a widely used aerosol thermodynamic model) in estimating aerosol acidity of coarse and fine particles. The M0 mode, which incorporates gas-phase data and runs the model in the forward mode, provided reasonable estimation of aerosol acidity for coarse and fine particles. Compared to M0, the M1 mode, which runs the model in the forward mode but does not include gas-phase data, may capture the general trend of aerosol acidity but underestimates pH for both coarse and fine particles; M2, which runs the model in the reverse mode, results in large errors in estimated aerosol pH for both coarse and fine particles and should not be used for aerosol acidity calculations. However, M1 significantly underestimates liquid water contents for both fine and coarse particles, while M2 provides reliable estimation of liquid water contents. In summary, our work highlights the importance of incorporating gas-aerosol partitioning when estimating coarse particle acidity, and thus may help improve our understanding of acidity of coarse particles.

Combustion aerosols and suspended dust with controlled processes in Lhasa: Elemental analysis and size distribution characteristics.

To explore air contamination resulting from special biomass combustion and suspended dust in Lhasa, the present study focused on the size distribution and chemical characteristics of particulate matter (PM) emission resulting from 7 types of non-fossil pollution sources. We investigated the concentration and size distribution of trace elements from 7 pollution sources collected in Lhasa. Combining Lhasa's atmospheric particulate matter data, enrichment factors (EFs) have been calculated to examine the potential impact of those pollution sources on the atmosphere quality of Lhasa. The highest mass concentration of total elements of biomass combustion appeared at PM0.4, and the second highest concentration existed in the size fraction 0.4-1 µm; the higher proportion (12 %) of toxic metals was produced by biomass combustion. The elemental composition of suspended dust and atmospheric particulate matter was close (except for As and Cd); the highest concentration of elements was all noted in PM2.5-10 (PM3-10). Potassium was found to be one of the main biomass markers. The proportion of Cu in suspended dust is significantly lower than that of atmospheric particulate matter (0.53 % and 3.75 %), which indicates that there are other anthropogenic sources. The EFs analysis showed that the Cr, Cu, Zn, and Pb produced by biomass combustion were highly enriched (EFs > 100) in all particle sizes. The EFs of most trace elements increased with decreasing particle size, indicating the greater influence of humanfactors on smaller particles.

Emission control status and future perspectives of diesel trucks in China.

Chinese diesel trucks are the main contributors to NOx and particulate matter (PM) vehicle emissions. An increase in diesel trucks could aggravate air pollution and damage human health. The Chinese government has recently implemented a series of emission control technologies and measures for air quality improvement. This paper summarizes recent control technologies and measures for diesel truck emissions in China and introduces the comprehensive application of control technologies and measures in Beijing-Tianjin-Hebei and surrounding regions. Remote online monitoring technology has been adopted according to the China VI standard for heavy-duty diesel trucks, and control measures such as transportation structure adjustment and heavy pollution enterprise classification control continue to support the battle action plan for pollution control. Perspectives and suggestions are provided for promoting pollution control and supervision of diesel truck emissions: adhere to the concept of overall management and control, vigorously promote the application of systematic and technological means in emission monitoring, continuously facilitate cargo transportation structure adjustment and promote new energy freight vehicles. This paper aims to accelerate the implementation of control technologies and measures throughout China. China is endeavouring to control diesel truck exhaust pollution. China is willing to cooperate with the world to protect the global ecological environment.

Comparative evaluation of single and multiple exposure to PM2.5 in respirable air on cardiac physiology, structure and function in a Wistar rat model.

Many studies have shown the negative relationship between long term exposure to PM2.5 and cardiac dysfunction. Recently, studies have shown that even a single exposure of PM2.5 from air sample in permissible range can induce very mild cardiac pathological changes. In the present study, we revisited the toxic effect of PM2.5 on rat heart by adopting single and multiple exposure durations. Female Wistar rats were exposed to PM2.5 at a concentration of 250 µg/m3 daily for 3 hr for single (1 day) and multiple (7, 14, 21 days) durations. The major pathological changes noted in 21 days exposed myocardium comprised of an elevated ST segment (the segment between the S wave and the T wave), development of cardiac fibrosis, hypertrophy, cardiac injury, tissue inflammation and declined cardiac function. With 14 days exposed heart, the electrocardiograms (ECG),data showed insignificantly declined heart rate and an increased QT (the time from the start of the Q wave to the end of the T wave) interval along with mild fibrosis, hypertrophy and lesser number of TUNEL positive cells. On the other hand, single- and 7-days exposure to PM2.5 did not impart any significant changes in the myocardium. To determine the reversibility potential of PM2.5 induced cardiotoxicity, a washout period of 24 hours was adopted and all observed changes in the myocardium were reversed till day 7, but not in 14- and 21-days exposed samples. Based on the above findings we concluded that PM2.5 associated cardiac dysfunction is the cumulative outcome of ineffective cardiac adaptive and repair process that accumulate additively over the time due to prolonged exposure durations.

Characterization and function of particulate organic matter: Evidence from lakes undergoing ecological restoration.

Particulate organic matter (POM) plays a crucial role in the organic composition of lakes; however, its characteristics remain poorly understood. This study aimed to characterize the structure and composition of POM in Lake Baiyangdian using many kinds of techniques and investigate the effects of different extracted forms of POM on water quality. The suspended particulate matter in the lake had complex compositions, with its components primarily derived from aquatic plants and their detritus. The organic matter content of the suspended particulate matter was relatively high (organic carbon content 27.29-145.94 g/kg) for the sum of three extractable states (water-extracted organic matter [WEOM], humic acid, and fulvic acid) and one stable bound state (humin). Spatial distribution analysis revealed that the POM content in the water increased from west to east, which was consistent with the water flow pattern influenced by the Baiyangdian water diversion project. Fluorescence spectroscopy analysis of the WEOM showed three prominent peaks with excitation/emission wavelengths similar to those of dissolved organic matter peaks. These peaks were potentially initial products of POM conversion into dissolved organic matter. Furthermore, the intensity of the WEOM fluorescence peak (total fluorescence peak intensity) was negatively correlated with the inorganic nitrogen concentration in water (p < 0.01), while the intensity of the HA fluorescence peak showed a positive correlation with the inorganic nitrogen concentration (p < 0.01). This suggested that exogenous organic matter inputs led to the diffusion of alkaline dissolved nitrogen from sediment into water, while degradation processes of aquatic plant debris contributed to the decrease in inorganic nitrogen concentrations in the water column. These findings enhance our understanding of POM characteristics in shallow lakes and the role of POM in shallow lake ecosystems.

A methodological framework for estimating ambient PM2.5 particulate matter concentrations in the UK.

Scientific evidence sustains PM2.5 particles' inhalation may generate harmful impacts on human beings' health; therefore, their monitoring in ambient air is of paramount relevance in terms of public health. Due to the limited number of fixed stations within the air quality monitoring networks, development of methodological frameworks to model ambient air PM2.5 particles is primordial to providing additional information on PM2.5 exposure and its trends. In this sense, this work aims to offer a global easily-applicable tool to estimate ambient air PM2.5 as a function of meteorological conditions using a multivariate analysis. Daily PM2.5 data measured by 84 fixed monitoring stations and meteorological data from ERA5 (ECMWF Reanalysis v5) reanalysis daily based data between 2000 and 2021 across the United Kingdom were attended to develop the suggested approach. Data from January 2017 to December 2020 were employed to build a mathematical expression that related the dependent variable (PM2.5) to predictor ones (sea-level pressure, planetary boundary layer height, temperature, precipitation, wind direction and speed), while 2021 data tested the model. Evaluation indicators evidenced a good performance of model (maximum values of RMSE, MAE and MAPE: 1.80 µg/m3, 3.24 µg/m3, and 20.63%, respectively), compiling the current legislation's requirements for modelling ambient air PM2.5 concentrations. A retrospective analysis of meteorological features allowed estimating ambient air PM2.5 concentrations from 2000 to 2021. The highest PM2.5 concentrations relapsed in the Mid- and Southlands, while Northlands sustained the lowest concentrations.

Impact of atmospheric particulate matter retention on physiological characters of five plant species under different pollution levels in Zhengzhou.

Atmospheric particulate matter (PM) pollution has become a major environmental risk, and green plants can mitigate air pollution by regulating their enzymatic activity, osmoregulatory substances, photosynthetic pigments, and other biochemical characteristics. The present investigation aims to evaluate the mitigation potential of five common evergreen tree species (Photinia serrulata, Ligustrum lucidum, Eriobotrya japonica, Euonymus japonicus, Pittosporum tobira) against air pollution and to assess the effect of dust retention on plant physiological functions exposed to three different pollution levels (road, campus, and park). The results found that the amount of dust retained per unit leaf area of the plants was proportional to the mass concentration of atmospheric particulate matter in the environment, and that dust accumulation was higher on the road and campus than in the park. There were significant differences in dust retention among the five tree species, with the highest leaf dust accumulation observed for E. japonica (5.45 g·m-2), and the lowest for P. tobira (1.53 g·m-2). In addition, the increase in PM adsorption by different plants was uneven with increasing pollution levels, with significant decreases in chlorophyll content, photosynthetic and transpiration rate. From a physiological perspective, P. tobira exhibited greater potential to respond to PM pollution. Biochemical indicators suggested that PM pollution caused changes in plant protective enzyme activities, with a decrease in superoxide dismutase (SOD) and peroxidase (POD) activities, as well as promoting membrane lipid peroxidation, and appropriate stress also enables plants to counteract oxidative damage. In particular, PM exposure also induced stomatal constriction. Overall, PM retention was significantly associated with physiological and photosynthetic traits. In conclusion, our study contributes to the understanding of the effects of PM on plant physiology. Furthermore, it also provides insights into the selection of plants that are tolerant to PM pollution.

Assessment of environmental and health risks of potentially toxic elements associated with desert dust particles affected by industrial activities in Isfahan metropolitan.

Dust particles and their associated compounds can adversely affect human health and ecosystems. The aim of this study was to investigate the concentration, health, and ecological risks of selected potentially toxic elements (e.g. Pb, Cd, Cr, Co, Cu, Zn, V, Ni, and As) bound to air particles generated by dust storms in the Sejzi plain desert area within the industrial district of Isfahan metropolitan, Iran. The enrichment factor revealed the highest values for Zn, Pb, and Cd which among them Zn showed the highest value (8.1) with the potential source of industrial activities confirmed by the integrated pollution index, accumulation coefficient, and ecological risk index. Regarding health risk analysis (non-cancer and cancer risks) the elements including Co, As, and Cr showed a significant risk for adults and children across all seasons. It's concluded that mitigation of air particles originated from both natural and industrial activities is necessary to reduce their relevant risks to human being and ecosystems in the region.

Application of machine learning and deep learning techniques in modeling the associations between air pollution and meteorological parameters in urban areas of tehran metropolis.

Tehran, the most crowded city in Iran, suffers from severe air pollution, particularly during the cold months. This research endeavored to examine the statistical relationships between criteria air pollutants (CO, NO2, SO2, O3, PM10, and PM2.5) and meteorological elements (temperature, rainfall, wind speed, relative humidity, air pressure, sunshine hours, solar radiation, and cloudiness), as well as assess and compare the efficacy of six different algorithms (multiple linear regression (MLR), generalized additive model (GAM), classification and regression trees (CART), random forest (RF), gradient boosting machine (GBM), and deep learning (DL)) in modeling pollutants and climatic factors responsible for variations in Tehran's air pollution levels from 2001 to 2021 using R 4.3.2 software. The results of this study showed that O3 was strongly affected by weather conditions, while other pollutants were mainly influenced by each other than by meteorological parameters and more extensive research is required to pinpoint the precise impact of human activity on these pollutant levels in Tehran. Also based on the predictive model performance evaluation and concerning the principle of parsimony, in half of the cases, the MLR outperformed other models, despite its seeming simplicity and principal assumptions dependence. In other situations, the GAM was a good substitute.

Ambient Air Pollution and Hospitalizations for Schizophrenia in China.

Importance: Schizophrenia episodes may be triggered by short-term environmental stimuli. Short-term increases in ambient air pollution levels may elevate the risk of schizophrenia episodes, yet few epidemiologic studies have examined this association. Objective: To investigate whether short-term increases in air pollution levels are associated with an additional risk of schizophrenia episodes, independent of absolute air pollution concentrations, and whether sustained increases in air pollution levels for several days are associated with more pronounced risks of schizophrenia episodes. Design, Setting, and Participants: This nationwide, population-based, time-stratified case-crossover study was performed based on hospitalization records for schizophrenia across 295 administrative divisions of prefecture-level or above cities in China. Records were extracted from 2 major health insurance systems from January 1, 2013, to December 31, 2017. Thirty-six cities with a small number of schizophrenia hospitalizations (n < 50) were excluded. Data analysis for this study was performed from January to March 2024. Exposure: Daily absolute concentrations of fine particulate matter (PM2.5), inhalable particulate matter (PM10), nitrogen dioxide, sulfur dioxide, ozone, and carbon monoxide were collected. Air pollution increases between neighboring days (APINs) were generated as the differences in absolute air pollution concentrations on the current day minus that on the previous day. Sustained increases (APIN ≥5 µg/m3 for PM2.5 and PM10, APIN ≥1 µg/m3 for nitrogen dioxide and sulfur dioxide, and APIN ≥0.05 mg/m3 for carbon monoxide) lasting for 1 or more to 4 or more days were defined for different air pollutants. Main Outcome and Measure: Patients with schizophrenia episodes were identified by principal discharge diagnoses of schizophrenia. A conditional logistic regression model was used to capture the associations of absolute concentrations, APINs, and sustained increase events for different air pollutants with risks of schizophrenia hospitalizations. Results: The study included 817 296 hospitalization records for schizophrenia across 259 Chinese cities (30.6% aged 0-39 years, 56.4% aged 40-64 years, and 13.0% aged ≥65 years; 55.04% male). After adjusting for the absolute concentrations of respective air pollutants, per-IQR increases in 6-day moving average (lag0-5) APINs of PM2.5, PM10, nitrogen dioxide, sulfur dioxide, and carbon monoxide were associated with increases of 2.37% (95% CI, 0.88%-3.88%), 2.95% (95% CI, 1.46%-4.47%), 4.61% (95% CI, 2.93%-6.32%), 2.16% (95% CI, 0.59%-3.76%), and 2.02% (95% CI, 0.39%-3.68%) in schizophrenia hospitalizations, respectively. Greater risks of schizophrenia hospitalizations were associated with sustained increases in air pollutants lasting for longer durations up to 4 or more days. Conclusions and Relevance: This case-crossover study of the association between ambient air pollution increases and schizophrenia hospitalizations provides novel evidence that short-term increases in ambient air pollution levels were positively associated with an elevated risk of schizophrenia episodes. Future schizophrenia prevention practices should pay additional attention to APINs, especially sustained increases in air pollution levels for longer durations, besides the absolute air pollution concentrations.

Unveiling the potential of a novel portable air quality platform for assessment of fine and coarse particulate matter: in-field testing, calibration, and machine learning insights.

Although low-cost air quality sensors facilitate the implementation of denser air quality monitoring networks, enabling a more realistic assessment of individual exposure to airborne pollutants, their sensitivity to multifaceted field conditions is often overlooked in laboratory testing. This gap was addressed by introducing an in-field calibration and validation of three PAQMON 1.0 mobile sensing low-cost platforms developed at the Mining and Metallurgy Institute in Bor, Republic of Serbia. A configuration tailored for monitoring PM2.5 and PM10 mass concentrations along with meteorological parameters was employed for outdoor measurement campaigns in Bor, spanning heating (HS) and non-heating (NHS) seasons. A statistically significant positive linear correlation between raw PM2.5 and PM10 measurements during both campaigns (R > 0.90, p ≤ 0.001) was observed. Measurements obtained from the uncalibrated NOVA SDS011 sensors integrated into the PAQMON 1.0 platforms exhibited a substantial and statistically significant correlation with the GRIMM EDM180 monitor (R > 0.60, p ≤ 0.001). The calibration models based on linear and Random Forest (RF) regression were compared. RF models provided more accurate descriptions of air quality, with average adjR2 values for air quality variables in the range of 0.70 to 0.80 and average NRMSE values between 0.35 and 0.77. RF-calibrated PAQMON 1.0 platforms displayed divergent levels of accuracy across different pollutant concentration ranges, achieving a data quality objective of 50% during both measurement campaigns. For PM2.5, uncertainty ( U r ) was below 50% for concentrations between 9.06 and 34.99 µg/m3 in HS and 5.75 and 17.58 µg/m3 in NHS, while for PM10, it stayed below 50% from 19.11 to 51.13 µg/m3 in HS and 11.72 to 38.86 µg/m3 in NHS.