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.

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.

Establishment and verification of anthropogenic speciated VOCs emission inventory of Central China.

Improving the accuracy of anthropogenic volatile organic compounds (VOCs) emission inventory is crucial for reducing atmospheric pollution and formulating control policy of air pollution. In this study, an anthropogenic speciated VOCs emission inventory was established for Central China represented by Henan Province at a 3 km × 3 km spatial resolution based on the emission factor method. The 2019 VOCs emission in Henan Province was 1003.5 Gg, while industrial process source (33.7%) was the highest emission source, Zhengzhou (17.9%) was the city with highest emission and April and August were the months with the more emissions. High VOCs emission regions were concentrated in downtown areas and industrial parks. Alkanes and aromatic hydrocarbons were the main VOCs contribution groups. The species composition, source contribution and spatial distribution were verified and evaluated through tracer ratio method (TR), Positive Matrix Factorization Model (PMF) and remote sensing inversion (RSI). Results show that both the emission results by emission inventory (EI) (15.7 Gg) and by TR method (13.6 Gg) and source contribution by EI and PMF are familiar. The spatial distribution of HCHO primary emission based on RSI is basically consistent with that of HCHO emission based on EI with a R-value of 0.73. The verification results show that the VOCs emission inventory and speciated emission inventory established in this study are relatively reliable.

Investigation of spatiotemporal distribution and formation mechanisms of ozone pollution in eastern Chinese cities applying convolutional neural network.

Severe ground-level ozone (O3) pollution over major Chinese cities has become one of the most challenging problems, which have deleterious effects on human health and the sustainability of society. This study explored the spatiotemporal distribution characteristics of ground-level O3 and its precursors based on conventional pollutant and meteorological monitoring data in Zhejiang Province from 2016 to 2021. Then, a high-performance convolutional neural network (CNN) model was established by expanding the moment and the concentration variations to general factors. Finally, the response mechanism of O3 to the variation with crucial influencing factors is explored by controlling variables and interpolating target variables. The results indicated that the annual average MDA8-90th concentrations in Zhejiang Province are higher in the northern and lower in the southern. When the wind direction (WD) ranges from east to southwest and the wind speed (WS) ranges between 2 and 3 m/sec, higher O3 concentration prone to occur. At different temperatures (T), the O3 concentration showed a trend of first increasing and subsequently decreasing with increasing NO2 concentration, peaks at the NO2 concentration around 0.02 mg/m3. The sensitivity of NO2 to O3 formation is not easily affected by temperature, barometric pressure and dew point temperature. Additionally, there is a minimum [Formula: see text] at each temperature when the NO2 concentration is 0.03 mg/m3, and this minimum [Formula: see text] decreases with increasing temperature. The study explores the response mechanism of O3 with the change of driving variables, which can provide a scientific foundation and methodological support for the targeted management of O3 pollution.

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.

Temporal variations in the short-term effects of ambient air pollution on cardiovascular and respiratory mortality: a pooled analysis of 380 urban areas over a 22-year period.

BACKGROUND: Ambient air pollution, including particulate matter (such as PM10 and PM2·5) and nitrogen dioxide (NO2), has been linked to increases in mortality. Whether populations' vulnerability to these pollutants has changed over time is unclear, and studies on this topic do not include multicountry analysis. We evaluated whether changes in exposure to air pollutants were associated with changes in mortality effect estimates over time. METHODS: We extracted cause-specific mortality and air pollution data collected between 1995 and 2016 from the Multi-Country Multi-City (MCC) Collaborative Research Network database. We applied a two-stage approach to analyse the short-term effects of NO2, PM10, and PM2·5 on cause-specific mortality using city-specific time series regression analyses and multilevel random-effects meta-analysis. We assessed changes over time using a longitudinal meta-regression with time as a linear fixed term and explored potential sources of heterogeneity and two-pollutant models. FINDINGS: Over 21·6 million cardiovascular and 7·7 million respiratory deaths in 380 cities across 24 countries over the study period were included in the analysis. All three air pollutants showed decreasing concentrations over time. The pooled results suggested no significant temporal change in the effect estimates per unit exposure of PM10, PM2·5, or NO2 and mortality. However, the risk of cardiovascular mortality increased from 0·37% (95% CI -0·05 to 0·80) in 1998 to 0·85% (0·55 to 1·16) in 2012 with a 10 µg/m3 increase in PM2·5. Two-pollutant models generally showed similar results to single-pollutant models for PM fractions and indicated temporal differences for NO2. INTERPRETATION: Although air pollution levels decreased during the study period, the effect sizes per unit increase in air pollution concentration have not changed. This observation might be due to the composition, toxicity, and sources of air pollution, as well as other factors, such as socioeconomic determinants or changes in population distribution and susceptibility. FUNDING: None.

[Air pollution and cardiovascular disease]. / Inquinamento dell'aria e malattie cardiovascolari.

Although there is substantial evidence on the harmful effects of air pollution on human health, these are scarcely considered in the general clinical practice and also in the context of cardiovascular disease prevention. In light of the numerous epidemiological and basic research studies that have demonstrated the unfavorable impact of air pollution on the cardiovascular system, this review aims to bring this aspect to the attention of clinicians. This work describes the main air polluting components that can contribute to the onset and progression of cardiovascular diseases. The pathophysiological mechanisms underlying the impact of pollutants on the cardiovascular system and the available evidence regarding their effect on cardiovascular risk factors are reported. This article also examines the evidence relating to the correlation between environmental pollutants and some specific cardiovascular diseases, including acute coronary syndromes, cerebrovascular diseases, heart failure, and arrhythmias. Finally, the possible strategies to be implemented to limit pollution-induced cardiovascular damage are analyzed.

Environmental injustice in the air quality for digital platform delivery workers in Bogotá, Colombia, 2021 / Injusticia ambiental en la calidad del aire para repartidores de plataformas digitales de Bogotá, Colombia, 2021

Introduction: Air quality is a matter of interest for public health due to its rapid deterioration in low- and middle-income countries and the effects of polluted air on the health of populations. Objective: To explore the air quality conditions in which digital platform delivery workers carry out their work, evaluating the localities of Kennedy and Usaquén in Bogotá, 2021. Materials and methods: We developed a mixed parallel convergent study based on four sources of information: 1) Ethnographic observation in five commercial locations of the two localities; 2) Monitoring of PM10 and PM2.5 in 56 delivery routes using a low-cost sensor; 3) Daily logs of the routes to support the device data interpretation, and 4) A semi-structured interview applied to the drivers to explore their danger perception during the routes. Results: We identified elements causing environmental injustice among digital platform delivery workers between the two study locations. The routes made by the delivery drivers in the locality of Kennedy registered higher concentrations of PM10 and PM2.5, compared to the values observed in Usaquén. The sources of air pollution identified by the delivery drivers through ethnographic observation and the router logbook showed the worst parameters in Kennedy. Conclusions: We evidenced that air quality, urban equipment, road infrastructure, mobile sources, and geospatial location are elements that mark the presence of environmental injustice for the digital platform delivery drivers in the studied localities. To reduce this inequity, it is necessary for digital delivery platforms and the district government to implement strategies that reduce the exposure and emission of air pollutants to protect the health of digital platform delivery workers.

Introducción: La calidad del aire es un asunto de interés para la salud pública por su rápido deterioro en los países de bajos y medianos ingresos, y los efectos del aire contaminado en la salud de las poblaciones. Objetivo: Explorar las condiciones de la calidad del aire en las que los repartidores de plataformas digitales desarrollaron su trabajo en las localidades de Kennedy y Usaquén de Bogotá durante el 2021. Materiales y métodos: Se llevó a cabo un estudio mixto, paralelo y convergente, basado en cuatro fuentes de información: 1) observación etnográfica en cinco ubicaciones comerciales de las dos localidades; 2) monitoreo de PM10 y PM2.5 en 56 rutas de reparto, empleando un equipo de bajo costo; 3) bitácoras diarias de los recorridos que apoyaron la interpretación de los datos del equipo, y 4) entrevista semiestructurada con el rutero para explorar sus percepciones frente a los peligros durante los recorridos. Resultados: Se identificaron diferencias en las condiciones de trabajo, las percepciones y las exposiciones a material particulado de los repartidores entre las dos localidades de estudio que constituyeron fuentes de injusticia ambiental. Los recorridos que realizaron los repartidores en la localidad de Kennedy registraron mayores concentraciones de PM10 y PM2.5. Las fuentes de contaminación atmosférica identificadas por los repartidores mostraron los peores parámetros en Kennedy. Conclusiones: Se evidenció que la calidad del aire, el equipamiento urbano, la infraestructura vial, las fuentes móviles y la ubicación geoespacial son elementos que marcan la presencia de injusticia ambiental para los repartidores. Para disminuir esta inequidad, es necesario que las plataformas de reparto digital y el gobierno distrital implementen estrategias que reduzcan la exposición y la emisión de contaminantes del aire con el fin de proteger la salud de los repartidores de plataformas.

Spatial consistency of co-exposure to air and surface water pollution and cancer in China.

Humans can be exposed to multiple pollutants in the air and surface water. These environments are non-static, trans-boundary and correlated, creating a complex network, and significant challenges for research on environmental hazards, especially in real-world cancer research. This article reports on a large study (377 million people in 30 provinces of China) that evaluated the combined impact of air and surface water pollution on cancer. We formulate a spatial evaluation system and a common grading scale for co-pollution measurement, and validate assumptions that air and surface water environments are spatially connected and that cancers of different types tend to cluster in areas where these environments are poorer. We observe "dose-response" relationships in both the number of affected cancer types and the cancer incidence with an increase in degree of co-pollution. We estimate that 62,847 (7.4%) new cases of cancer registered in China in 2016 were attributable to air and surface water pollution, and the majority (69.7%) of these excess cases occurred in areas with the highest level of co-pollution. The findings clearly show that the environment cannot be considered as a set of separate entities. They also support the development of policies for cooperative environmental governance and disease prevention.

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.

Features of extreme PM 2.5 pollution and its influencing factors: evidence from China.

Extreme PM 2.5 pollution has become a significant environmental problem in China in recent years, which is hazardous to human health and daily life. Noticing the importance of investigating the causes of extreme PM 2.5 pollution, this paper classifies cities across China into eight categories (four groups plus two scenarios) based on the generalized extreme value (GEV) distribution using hourly station-level PM 2.5 concentration data, and a series of multi-choice models are employed to assess the probabilities that cities fall into different categories. Various factors such as precursor pollutants and socio-economic factors are considered after controlling for meteorological conditions in each model. It turns out that SO 2 concentration, NO 2 concentration, and population density are the top three factors contributing most to the log ratios. Moreover, in both left- and right-skewed cases, the influence of a one-unit increase of SO 2 concentration on the relative probability of cities falling into different groups shows an increasing trend, while those of NO 2 concentration show a decreasing trend. At the same time, the higher the extreme pollution level, the bigger the effect of SO 2 and NO 2 concentrations on the probability of cities falling into normalized scenarios. The multivariate logit model is used for prediction and policy simulations. In summary, by analyzing the influences of various factors and the heterogeneity of their influence patterns, this paper provides valuable insights in formulating effective emission reduction policies.

Research on characteristics and influencing factors of road dust emission in a southern city in China.

One of the primary causes of urban atmospheric particulate matter, which is harmful to human health in addition to affecting air quality and atmospheric visibility, is road dust. This study used online monitoring equipment to examine the characteristics of road dust emissions, the effects of temperature, humidity, and wind speed on road dust, as well as the correlation between road and high-space particulate matter concentrations. A section of a real road in Jinhua City, South China, was chosen for the study. The findings demonstrate that the concentration of road dust particles has a very clear bimodal single-valley distribution throughout the day, peaking between 8:00 and 11:00 and 19:00 and 21:00 and troughing between 14:00 and 16:00. Throughout the year, there is a noticeable seasonal change in the concentration of road dust particles, with the highest concentration in the winter and the lowest in the summer. Simultaneously, it has been discovered that temperature and wind speed have the most effects on particle concentration. The concentration of road dust particles reduces with increasing temperature and wind speed. The particle concentrations of road particles and those from urban environmental monitoring stations have a strong correlation, although the trend in the former is not entirely consistent, and the changes in the former occur approximately 1 h after the changes in the latter.

Air Pollution and Parkinson Disease in a Population-Based Study.

Importance: The role of air pollution in risk and progression of Parkinson disease (PD) is unclear. Objective: To assess whether air pollution is associated with increased risk of PD and clinical characteristics of PD. Design, Setting, and Participants: This population-based case-control study included patients with PD and matched controls from the Rochester Epidemiology Project from 1998 to 2015. Data were analyzed from January to June 2024. Exposures: Mean annual exposure to particulate matter with a diameter of 2.5 µm or less (PM2.5) from 1998 to 2015 and mean annual exposure to nitrogen dioxide (NO2) from 2000 to 2014. Main Outcomes and Measures: Outcomes of interest were PD risk, all-cause mortality, presence of tremor-predominant vs akinetic rigid PD, and development of dyskinesia. Models were adjusted for age, sex, race and ethnicity, year of index, and urban vs rural residence. Results: A total of 346 patients with PD (median [IQR] age 72 [65-80] years; 216 [62.4%] male) were identified and matched on age and sex with 4813 controls (median [IQR] age, 72 [65-79] years, 2946 [61.2%] male). Greater PM2.5 exposure was associated with increased PD risk, and this risk was greatest after restricting to populations within metropolitan cores (odds ratio [OR], 1.23; 95% CI, 1.11-1.35) for the top quintile of PM2.5 exposure compared with the bottom quintile. Greater NO2 exposure was also associated with increased PD risk when comparing the top quintile with the bottom quintile (OR, 1.13; 95% CI, 1.07-1.19). Air pollution was associated with a 36% increased risk of akinetic rigid presentation (OR per each 1-µg/m3 increase in PM2.5, 1.36; 95% CI, 1.02-1.80). In analyses among patients with PD only, higher PM2.5 exposure was associated with greater risk for developing dyskinesia (HR per 1-µg/m3 increase in PM2.5, 1.42; 95% CI, 1.17-1.73), as was increased NO2 exposure (HR per 1 µg/m3 increase in NO2, 1.13; 95% CI, 1.06-1.19). There was no association between PM2.5 and all-cause mortality among patients with PD. Conclusions and Relevance: In this case-control study of air pollution and PD, higher levels of PM2.5 and NO2 exposure were associated with increased risk of PD; also, higher levels of PM2.5 exposure were associated with increased risk of developing akinetic rigid PD and dyskinesia compared with patients with PD exposed to lower levels. These findings suggest that reducing air pollution may reduce risk of PD, modify the PD phenotype, and reduce risk of dyskinesia.

PM2.5 exposure disparities persist despite strict vehicle emissions controls in California.

As policymakers increasingly focus on environmental justice, a key question is whether emissions reductions aimed at addressing air quality or climate change can also ameliorate persistent air pollution exposure disparities. We examine evidence from California's aggressive vehicle emissions control policy from 2000 to 2019. We find a 65% reduction in modeled statewide average exposure to PM2.5 from on-road vehicles, yet for people of color and overburdened community residents, relative exposure disparities increased. Light-duty vehicle emissions are the main driver of the exposure and exposure disparity, although smaller contributions from heavy-duty vehicles especially affect some overburdened groups. Our findings suggest that a continued trend of emissions reductions will likely reduce concentrations and absolute disparity but may not reduce relative disparities without greater attention to the systemic factors leading to this disparity.

The impact of data imputation on air quality prediction problem.

With rising environmental concerns, accurate air quality predictions have become paramount as they help in planning preventive measures and policies for potential health hazards and environmental problems caused by poor air quality. Most of the time, air quality data are time series data. However, due to various reasons, we often encounter missing values in datasets collected during data preparation and aggregation steps. The inability to analyze and handle missing data will significantly hinder the data analysis process. To address this issue, this paper offers an extensive review of air quality prediction and missing data imputation techniques for time series, particularly in relation to environmental challenges. In addition, we empirically assess eight imputation methods, including mean, median, kNNI, MICE, SAITS, BRITS, MRNN, and Transformer, to scrutinize their impact on air quality data. The evaluation is conducted using diverse air quality datasets gathered from numerous cities globally. Based on these evaluations, we offer practical recommendations for practitioners dealing with missing data in time series scenarios for environmental data.

Modeling health outcomes of air pollution in the Middle East by using support vector machines and neural networks.

This study investigates the impact of air pollution on health outcomes in Middle Eastern countries, a region facing severe environmental challenges. As such, these are important in an effort to add up to policy-level as well as interventional changes that can be put in practice in the area of public health. Numeration analysis and association with health parameters was carried out by using Analytical tools such as, AIR Data, ARIMA,ANN, SVM and Exponential smoothing. Amongst the models, Support Vector Machine came again on top, with high accuracy yielding Mean Absolute Percentage Error of approximately 1%. Mortality of Air pollution in Qat from the case of Mortality of Air Pollution in Qatar is 959 while Auto regressive Integrated Moving average is 11.096, Exponential Smoothing 9.892 and Artificial Neural Networks are the source of inspiration for the development of this paper 4.61. The above perceptions indicate that there is need to adapt modeling strategies depending on the context and establish that it is possible to implement ML models in public health planning basket. This paper publishes the methodological frameworks for the purpose of modeling and analysis of the EHDs and serves as policy prescription for the policy makers to intending to reduce the effects of air borne pollution on health.

The effect of air pollution quality on lung cancer rates in middle-income and high-income countries: a panel data analysis approach.

Background: Air pollution is one of the biggest problems in societies today. The intensity of indoor and outdoor air pollutants and the urbanization rate can cause or trigger many different diseases, especially lung cancer. In this context, this study's aim is to reveal the effects of the indoor and outdoor air pollutants, and urbanization rate on the lung cancer cases. Methods: Panel data analysis method is applied in this study. The research includes the period between 1990 and 2019 as a time series and the data type of the variables is annual. The dependent variable in the research model is lung cancer cases per 100,000 people. The independent variables are the level of outdoor air pollution, air pollution level indoor environment and urbanization rate of countries. Results: In the modeling developed for the developed country group, it is seen that the variable with the highest level of effect on lung cancer is the outdoor air pollution level. Conclusions: In parallel with the development of countries, it has been determined that the increase in industrial production wastes, in other words, worsening the air quality, may potentially cause an increase in lung cancer cases. Indoor air quality is also essential for human health; negative changes in this variable may negatively impact individuals' health, especially lung cancer.

Too little or too much? Exploring the effectiveness of different policies in air pollution control from technical and non-technical pathways.

Most environmental policy studies focus on the technical pathway effect but ignore the non-technical pathway. This paper analyzes the synergistic governance effects of three types of environmental policies on the technical and non-technical pathways. The super-efficient slacks-based measure-data envelopment analysis (SBM-DEA) assesses the green total factor productivity, while the Malmquist index decomposes into pure technical efficiency. The findings indicate that: (1) command-and-control policy has the 'too-little-of-a-good-thing' effect, but the policy intensity in most Chinese provinces is strong enough to reduce air pollution, while market-based incentive policy may be 'too-much-of-a-good-thing', but Chinese provinces have not reached the inflection point; (2) there are considerable differences in the environmental effects of different policies through technical and non-technical pathways; (3) different policies have various focuses. Command-and-control policy focuses on the non-technical pathway, whereas market-based incentive policy can induce technological progress.

Geospatial modelling of ambient air pollutants and chronic obstructive pulmonary diseases at regional scale in Pakistan.

Pakistan is among the South Asian countries mostly vulnerable to the negative health impacts of air pollution. In this context, the study aimed to analyze the spatiotemporal patterns of chronic obstructive pulmonary disease (COPD) incidence and its relationship with air pollutants including aerosol absorbing index (AAI), carbon monoxide, sulfur dioxide (SO2), and nitrogen dioxide. Spatial scan statistics were employed to identify temporal, spatial, and spatiotemporal clusters of COPD. Generalized linear regression (GLR) and random forest (RF) models were utilized to evaluate the linear and non-linear relationships between COPD and air pollutants for the years 2019 and 2020. The findings revealed three spatial clusters of COPD in the eastern and central regions, with a high-risk spatiotemporal cluster in the east. The GLR identified a weak linear relationship between the COPD and air pollutants with R2 = 0.1 and weak autocorrelation with Moran's index = -0.09. The spatial outcome of RF model provided more accurate COPD predictions with improved R2 of 0.8 and 0.9 in the respective years and a very low Moran's I = -0.02 showing a random residual distribution. The RF findings also suggested AAI and SO2 to be the most contributing predictors for the year 2019 and 2020. Hence, the strong association of COPD clusters with some air pollutants highlight the urgency of comprehensive measures to combat air pollution in the region to avoid future health risks.

Air pollution and risk of 32 health conditions: outcome-wide analyses in a population-based prospective cohort in Southwest China.

BACKGROUND: Uncertainty remains about the long-term effects of air pollutants (AP) on multiple diseases, especially subtypes of cardiovascular disease (CVD). We aimed to assess the individual and joint associations of fine particulate matter (PM2.5), along with its chemical components, nitrogen dioxide (NO2) and ozone (O3), with risks of 32 health conditions. METHODS: A total of 17,566 participants in Sichuan Province, China, were included in 2018 and followed until 2022, with an average follow-up period of 4.2 years. The concentrations of AP were measured using a machine-learning approach. The Cox proportional hazards model and quantile g-computation were applied to assess the associations between AP and CVD. RESULTS: Per interquartile range (IQR) increase in PM2.5 mass, NO2, O3, nitrate, ammonium, organic matter (OM), black carbon (BC), chloride, and sulfate were significantly associated with increased risks of various conditions, with hazard ratios (HRs) ranging from 1.06 to 2.48. Exposure to multiple air pollutants was associated with total cardiovascular disease (HR 1.75, 95% confidence intervals (CIs) 1.62-1.89), hypertensive diseases (1.49, 1.38-1.62), cardiac arrests (1.52, 1.30-1.77), arrhythmia (1.76, 1.44-2.15), cerebrovascular diseases (1.86, 1.65-2.10), stroke (1.77, 1.54-2.03), ischemic stroke (1.85, 1.61-2.12), atherosclerosis (1.77, 1.57-1.99), diseases of veins, lymphatic vessels, and lymph nodes (1.32, 1.15-1.51), pneumonia (1.37, 1.16-1.61), inflammatory bowel diseases (1.34, 1.16-1.55), liver diseases (1.59, 1.43-1.77), type 2 diabetes (1.48, 1.26-1.73), lipoprotein metabolism disorders (2.20, 1.96-2.47), purine metabolism disorders (1.61, 1.38-1.88), anemia (1.29, 1.15-1.45), sleep disorders (1.54, 1.33-1.78), renal failure (1.44, 1.21-1.72), kidney stone (1.27, 1.13-1.43), osteoarthritis (2.18, 2.00-2.39), osteoporosis (1.36, 1.14-1.61). OM had max weights for joint effects of AP on many conditions. CONCLUSIONS: Long-term exposure to increased levels of multiple air pollutants was associated with risks of multiple health conditions. OM accounted for substantial weight for these increased risks, suggesting it may play an important role in these associations.