Quantitative association analysis between PM2.5 concentration and factors on industry, energy, agriculture, and transportation

Effect of high-level PM2.5 on survival in lung cancer: a multicenter cohort study from Hebei Province, China

Globally, air pollution is the fourth leading risk factor for death, while lung cancer (LC) is the leading cause of cancer-related death. The aim of this study was to explore the prognostic factors of LC and the influence of high fine particulate matter (PM_2.5) on LC survival. Data on LC patients were collected from 133 hospitals across 11 cities in Hebei Province from 2010 to 2015, and survival status was followed up until 2019. The personal PM_2.5 exposure concentration (μg/m³) was matched according to the patient's registered address, calculated from a 5-year average for every patient, and stratified into quartiles. The Kaplan-Meier method was used to estimate overall survival (OS), and Cox's proportional hazard regression model was used to estimate hazard ratios (HRs) with 95% confidence intervals (CIs). The 1-, 3-, and 5-year OS rates of the 6429 patients were 62.9%, 33.2%, and 15.2%, respectively. Advanced age (75 years or older: HR = 2.34, 95% CI: 1.25-4.38), subsite at overlapping (HR = 4.35, 95% CI: 1.70-11.1), poor/undifferentiated differentiation (HR = 1.71, 95% CI: 1.13-2.58), and advanced stages (stage III: HR = 2.53, 95% CI: 1.60-4.00; stage IV: HR = 4.00, 95% CI: 2.63-6.09) were risk factors for survival, while receiving surgical treatment was a protective factor (HR = 0.60, 95% CI: 0.44-0.83). Patients exposed to light pollution had the lowest risk of death with a 26-month median survival time. The risk of death in LC patients was greatest at PM_2.5 concentrations of 98.7-108.9 μg/m³, especially for patients at advanced stage (HR = 1.43, 95% CI: 1.29-1.60). Our study indicates that the survival of LC is severely affected by relatively high levels of PM_2.5 pollution, especially in those with advanced-stage cancer.

Examining the Effects of Socioeconomic Development on Fine Particulate Matter (PM2.5) in China's Cities Based on Spatial Autocorrelation Analysis and MGWR Model

Understanding the characteristics of PM2.5 and its socioeconomic factors is crucial for managing air pollution. Research on the socioeconomic influences of PM2.5 has yielded several results. However, the spatial heterogeneity of the effect of various socioeconomic factors on PM2.5 at different scales has yet to be studied. This paper collated PM2.5 data for 359 cities in China from 2005 to 2020, as well as socioeconomic data: GDP per capita (GDPP), secondary industry proportion (SIP), number of industrial enterprise units above the scale (NOIE), general public budget revenue as a proportion of GDP (PBR), and population density (PD). The spatial autocorrelation and multiscale geographically weighted regression (MGWR) model was used to analyze the spatiotemporal heterogeneity of PM2.5 and explore the impact of different scales of economic factors. Results show that the overall economic level was developing well, with a spatial distribution trend of high in the east and low in the west. With a large positive spatial correlation and a highly concentrated clustering pattern, the PM2.5 concentration declined in 2020. Secondly, the OLS model's statistical results were skewed and unable to shed light on the association between economic factors and PM2.5. Predictions from the GWR and MGWR models may be more precise than those from the OLS model. The scales of the effect were produced by the MGWR model's variable bandwidth and regression coefficient. In particular, the MGWR model's regression coefficient and variable bandwidth allowed it to account for the scale influence of economic factors; it had the highest adjusted R2 values, smallest AICc values, and residual sums of squares. Lastly, the PBR had a clear negative impact on PM2.5, whereas the negative impact of GDPP was weak and positively correlated in some western regions, such as Gansu and Qinghai provinces. The SIP, NOIE, and PD were positively correlated with PM2.5 in most regions. Our findings can serve as a theoretical foundation for researching the associations between PM2.5 and socioeconomic variables, and for encouraging the coequal growth of the economy and the environment.

Comprehensive evaluation method of urban air quality statistics based on environmental monitoring data and its application

Air quality monitoring is effective for timely understanding of the current air quality status of a region or city. Currently, the huge volume of environmental monitoring data, which has reasonable real-time performance, provides strong support for in-depth analysis of air pollution characteristics and causes. However, in the era of big data, to meet current demands for fine management of the atmospheric environment, it is important to explore the characteristics and causes of air pollution from multiple aspects for comprehensive and scientific evaluation of air quality. This study reviewed and summarized air quality evaluation methods on the basis of environmental monitoring data statistics during the 13th Five-Year Plan period, and evaluated the level of air pollution in the Beijing-Tianjin-Hebei region and its surrounding areas (i.e., the "2+26" region) during the period of the three-year action plan to fight air pollution. We suggest that air quality should be comprehensively, deeply, and scientifically evaluated from the aspects of air pollution characteristics, causes, and influences of meteorological conditions and anthropogenic emissions. It is also suggested that a three-year moving average be introduced as one of the evaluation indexes of long-term change of pollutants. Additionally, both temporal and spatial differences should be considered when removing confounding meteorological factors.

Regional interaction of lung cancer incidence influenced by PM2.5 in China

PM_2.5 is the key pollutant threatening human health and can even cause lung cancer. Pollution is the most serious problem in China with its fast industrialisation, urbanisation and high population density. This pollutant is conveyed through the atmosphere, trade and the embodied emission flow amongst regions. Scientific evaluation of the responsibility for regional lung cancer by considering both internal and external influences seems to be meaningful in addressing regional inequity. This study develops a relatively convenient and practical method to evaluate the regional inequity reflected by lung cancer associated with PM_2.5 pollution in China. Results show that PM_2.5 emissions and concentrations have similar distribution patterns: high values were predominant in the east and south where has high population density, while the west had low values. The cancer incidence rate showed high values mainly in eastern and central China. At a provincial scale, the lung cancer incidence rate was significantly correlated with PM_2.5 concentration levels, and a high correlation was also found between PM_2.5 concentration and emissions, indicating that emission reduction is the key to lung cancer prevention. Due to domestic trade, some developed regions more pulled lung cancer in less developed regions, and some less developed regions also have an obvious influence on external regions. Spatially, provinces in northern and central China are always more influenced by external regions. Lung cancer inequity analysis shows that coastline regions are more advantaged, while the reverse applies to inland China. The central government needs to further strengthen regional coordinated development measures, such as economic compensation for medical care and adjustments to industry structure. It should optimise spatial allocation and comprehensively consider regional inequity and character.

Exploring the convergence patterns of PM2.5 in Chinese cities

Economic development and ongoing urbanization are usually accompanied by severe haze pollution. Revealing the spatial and temporal evolution of haze pollution can provide a powerful tool for formulating sustainable development policies. Previous studies mostly discuss the differences in the level of PM2.5 among regions, but have paid little attention to the change rules of such differences and their clustering patterns over long periods. Therefore, from the perspective of club convergence, this study employs the log t regression test and club clustering algorithm proposed by Phillips and Sul (Econometrica 75(6):1771-1855, 2007. 10.1111/j.1468-0262.2007.00811.x) to empirically examine the convergence characteristics of PM2.5 concentrations in Chinese cities from 1998 to 2016. This study found that there was no evidence of full panel convergence, but supported one divergent group and eleven convergence clubs with large differences in mean PM2.5 concentrations and growth rates. The geographical distribution of these clubs showed significant spatial dependence. In addition, certain meteorological and socio-economic factors predominantly determined the convergence club for each city.

Factors Underlying Spatiotemporal Variations in Atmospheric PM2.5 Concentrations in Zhejiang Province, China

Fine particulate matter in the lower atmosphere (PM2.5) continues to be a major public health problem globally. Identifying the key contributors to PM2.5 pollution is important in monitoring and managing atmospheric quality, for example, in controlling haze. Previous research has been aimed at quantifying the relationship between PM2.5 values and their underlying factors, but the spatial and temporal dynamics of these factors are not well understood. Based on random forest and Shapley additive explanation (SHAP) algorithms, this study analyses the spatiotemporal variations in selected key factors influencing PM2.5 in Zhejiang Province, China, for the period 2000–2019. The results indicate that, while factors influencing PM2.5 varied significantly during the period studied, SHAP values suggest that there is consistency in their relative importance as follows: meteorological factors (e.g., atmospheric pressure) > socioeconomic factors (e.g., gross domestic product, GDP) > topography and land cover factors (e.g., elevation). The contribution of GDP and transportation factors initially increased but has declined in the recent past, indicating that economic and infrastructural development does not necessarily result in increased PM2.5 concentrations. Vegetation productivity, as indicated by changes in NDVI, is demonstrated to have become more important in improving air quality, and the area of the province over which it constrains PM2.5 concentrations has increased between 2000 and 2019. Mapping of SHAP values suggests that, although the relative importance of industrial emissions has declined during the period studied, the actual area positively impacted by such emissions has actually increased. Despite developments in government policy, greater efforts to conserve energy and reduce emissions are still needed. The study further demonstrates that the combination of random forest and SHAP methods provides a valuable means to identify regional differences in key factors affecting atmospheric PM2.5 values and offers a reliable reference for pollution control strategies.

Pathways of China's PM2.5 air quality 2015–2060 in the context of carbon neutrality

Clean air policies in China have substantially reduced particulate matter (PM_2.5) air pollution in recent years, primarily by curbing end-of-pipe emissions. However, reaching the level of the World Health Organization (WHO) guidelines may instead depend upon the air quality co-benefits of ambitious climate action. Here, we assess pathways of Chinese PM_2.5 air quality from 2015 to 2060 under a combination of scenarios that link global and Chinese climate mitigation pathways (i.e. global 2°C- and 1.5°C-pathways, National Determined Contributions (NDC) pledges and carbon neutrality goals) to local clean air policies. We find that China can achieve both its near-term climate goals (peak emissions) and PM_2.5 air quality annual standard (35 μg/m³) by 2030 by fulfilling its NDC pledges and continuing air pollution control policies. However, the benefits of end-of-pipe control reductions are mostly exhausted by 2030, and reducing PM_2.5 exposure of the majority of the Chinese population to below 10 μg/m³ by 2060 will likely require more ambitious climate mitigation efforts such as China's carbon neutrality goals and global 1.5°C-pathways. Our results thus highlight that China's carbon neutrality goals will play a critical role in reducing air pollution exposure to the level of the WHO guidelines and protecting public health.

Association of temporary Environmental Protection Agency regulation suspension with industrial economic viability and local air quality in California, United States

BACKGROUND

This study seeks to answer two questions about the impacts of the 2020 Environmental Protection Agency's enforcement regulation rollbacks: is this suspension bolstering the economic viability of industries as oil and manufacturing executives claim they will and are these regulations upholding the agency's mission of protecting the environment?

RESULTS

To answer the former question, we utilized 6 months of state employment level data from California, United States, as a method of gauging the economic health of agency-regulated industries. We implemented a machine learning model to predict weekly employment data and a t-test to indicate any significant changes in employment. We found that, following California's state-issued stay-at-home order and the agency's regulation suspension, oil and certain manufacturing industries had statistically significant lower employment values.To answer the latter question, we used 10 years of PM_2.5 levels in California, United States, as a metric for local air quality and treatment-control county pairs to isolate the impact of regulation rollbacks from the impacts of the state lockdown. Using the agency's data, we performed a t-test to determine whether treatment-control county pairs experienced a significant change in PM_2.5 levels. Even with the statewide lockdown-a measure we hypothesized would correlate with decreased mobility and pollution levels-in place, counties with oil refineries experienced the same air pollution levels when compared to historical data averaged from the years 2009 to 2019.

CONCLUSIONS

In contrast to the expectation that the suspension would improve the financial health of the oil and manufacturing industry, we can conclude that these industries are not witnessing economic growth with the suspension and state shutdown in place. Additionally, counties with oil refineries could be taking advantage of these rollbacks to continue emitting the same amount of PM_2.5, in spite of state lockdowns. For these reasons, we ask international policymakers to reconsider the suspension of enforcement regulations as these actions do not fulfill their initial expectations. We recommend the creation and maintenance of pollution control and prevention programs that develop emission baselines, mandate the construction of pollution databases, and update records of pollution emissions.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s12302-021-00489-9.

A new approach to evaluate regional inequity determined by PM2.5 emissions and concentrations

PM_2.5 is one of the most severe types of air pollution that threatens human health. Its emissions have a notable spillover effect once released into the atmosphere and transported. In domestic trade, PM_2.5 emissions can be indirectly imported from external regions. Thus, regional inequity caused by PM_2.5 needs to be integrated and comprehensively estimated. Based on PM_2.5 emissions/concentrations grid maps and an input-output model, this study first examined the temporal-spatial changes in PM_2.5 emissions/concentrations across China. Additionally, a detailed relationship between PM_2.5 emissions and concentrations was examined at multiple scales, both temporal and spatial. Finally, this study developed a new approach with which to evaluate regional inequity. The results show that PM_2.5 emissions and concentrations increased between 1990 and 2012 and 1998 and 2016, respectively; the increase was more obvious for PM_2.5 emissions. Spatially, a rapid increase in PM_2.5 emissions was observed in the North China Plain and the Sichuan Basin. Between 1998 and 2012, the distribution of PM_2.5 concentrations was similar to that of emissions; however, between 2013 and 2016, 46.6% of the total area showed a decrease, mainly in the central and southern parts of China. Relationship analysis revealed that PM_2.5 emissions and concentrations are closely correlated in both time and space. There was obvious regional inequity among provinces; developed regions always imported considerably more PM_2.5 emissions from undeveloped regions than they exported. Overall, the regional inequity estimation framework shows that provinces along the coastline, especially developed provinces, have advantages under the regional inequity estimation framework, while most of the inland regions have disadvantages, especially in the west and north.

The Context-Dependent Effect of Urban Form on Air Pollution: A Panel Data Analysis

There have been debates and a lack of understanding about the complex effects of urban-scale urban form on air pollution. Based on the remotely sensed data of 150 cities in the Beijing-Tianjin-Hebei agglomeration in China from 2000 to 2015, we studied the effects of urban form on fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations from multiple perspectives. The panel models show that the elastic coefficients of aggregation index and fractal dimension are the highest among all factors for the whole region. Population density, aggregation index, and fractal dimension have stronger influences on air pollution in small cities, while area size demonstrates the opposite effect. Population density has a stronger impact on medium/high-elevation cities, while night light intensity (NLI), fractal dimension, and area size show the opposite effect. Low road network density can enlarge the influence magnitude of NLI and population density. The results of the linear regression model with multiplicative interactions provide evidence of interactions between population density and NLI or aggregation index. The slope of the line that captures the relationship between NLI on PM2.5 is positive at low levels of population density, flat at medium levels of population density, and negative at high levels of population density. The study results also show that when increasing the population density, the air pollution in a city with low economic and low morphological aggregation degrees will be impacted more greatly.