Modeling the impacts of policy measures on resident's PM2.5 reduction behavior: an agent-based simulation analysis.

With the rapid economic growth of China, the increasingly serious environmental problems of haze pollution have become a large concern. Urban resident's PM2.5 reduction behavior contributes significantly to Chinese haze pollution control. Resident-level policy measures are beneficial for encouraging residents to engage in PM2.5 reduction behaviors. The current research aims to explore the long-term intervention effects of three types of policies (i.e., command and control policies, economic incentive policies and education-guided policies) on resident's PM2.5 reduction intention and actual behavior. Based on the agent-based modeling and simulation approach, a resident's PM2.5 reduction behavioral simulation model is developed, and data adopted from a questionnaire survey are analyzed. The simulation results show that resident's PM2.5 reduction intention is motivated by the interactions among resident agents, and it eventually stabilizes at a higher level (from 4.11 to 4.48). Moreover, the effects of the three types of policy measures on PM2.5 reduction behavior vary depending on the specific scenarios. With respect to single-policy scenarios, these policies all enhance the actual resident's PM2.5 reduction behavior over the long term. The effects of command and control policies (M = 3.42) and education-guided policies (M = 3.44) are much better than those of the economic incentive policies (M = 3.15). Regarding policy combination scenarios, a combination of economic incentive policies and education-guided policies (MII = 4.15) has a remarkable promotional effect over others for encouraging residents to conduct PM2.5 reduction behaviors. Based on the results, implications and suggestions for improving current resident-level PM2.5 reduction policies and encouraging resident's PM2.5 reduction behavior are provided.

Achievements and challenges in improving air quality in China: Analysis of the long-term trends from 2014 to 2022.

Due to the implementation of air pollution control measures in China, air quality has significantly improved, although there are still additional issues to be addressed. This study used the long-term trends of air pollutants to discuss the achievements and challenges in further improving air quality in China. The Kolmogorov-Zurbenko (KZ) filter and multiple-linear regression (MLR) were used to quantify the meteorology-related and emission-related trends of air pollutants from 2014 to 2022 in China. The KZ filter analysis showed that PM2.5 decreased by 7.36 ± 2.92% yr-1, while daily maximum 8-h ozone (MDA8 O3) showed an increasing trend with 3.71 ± 2.89% yr-1 in China. The decrease in PM2.5 and increase in MDA8 O3 were primarily attributed to changes in emission, with the relative contribution of 85.8% and 86.0%, respectively. Meteorology variations, including increased ambient temperature, boundary layer height, and reduced relative humidity, also contributed to the reduction of PM2.5 and the enhancement of MDA8 O3. The emission-related trends of PM2.5 and MDA8 O3 exhibited continuous decrease and increase, respectively, from 2014 to 2022, while the variation rates slowed during 2018-2020 compared to that during 2014-2017, highlighting the challenges in further improving air quality, particularly in simultaneously reducing PM2.5 and O3. This study recommends reducing NH3 emissions from the agriculture sector in rural areas and transport emissions in urban areas to further decrease PM2.5 levels. Addressing O3 pollution requires the reduction of O3 precursor gases based on site-specific atmospheric chemistry considerations.

Intricate synergistic effects between air pollution and carbon emission: An emerging evidence from China.

Due to global climate change and intensifying anthropogenic pollution, China confronts the dual challenge of controlling particulate matter 2.5 µm (PM2.5) pollution and reducing carbon emissions. Quantifying the characteristics of PM2.5 concentrations and CO2 emissions, as well as identifying the driving factors and synergistic effects of PM2.5 reduction and CO2 mitigation, are crucial steps in promoting sustainable urban development and achieving the Sustainable Development Goals (SDGs) in China. In this study, we selected 168 cities as our case-study, and quantified spatial characteristics of PM2.5 concentrations and CO2 emissions from 2015 to 2020 in China. Then we analyzed driving factors affecting the spatial heterogeneity of PM2.5 reduction and CO2 mitigation applying Multi-scale Geographically Weighted Regression (MGWR) model. By employing coupling coordination degree (CCD) model, we further detected the spatiotemporal evolution patterns of the synergistic effects between PM2.5 reduction and CO2 mitigation in key Chinese cities. The result showed that: (a) From 2015 to 2020, PM2.5 concentrations experienced a significant reduction from 59.78 µg/m3 to 49.83 µg/m3, while CO2 emissions increased from 44.88 × 106 t in 2015 to 45.77 × 106 t in 2020; (b) Green economy efficiency (gee), government attention (gover), and environmental regulation (envir) demonstrate the most pronounced synergistic effect on pollution reduction and carbon mitigation, with the drivers exhibiting obvious spatial heterogeneity; (c) The overall coupling coordination level of PM2.5 pollution and CO2 emissions in China dropped from 0.49 in 2015 to 0.46 in 2020, and the coupling coordination grade in northern cities was notably higher than that in southern cities. The result enhances our understanding of spatiotemporal patterns of synergistic effects between PM2.5 reduction and CO2 mitigation, and provides the theoretical basis for policy decision-making to realize pollution decrease and carbon neutral and regional environment governance.

Inorganic PM2.5 reduction in Kanto, Japan: The role of ammonia and its emission sources control strategies.

Ammonia (NH3) is attracting attention as a carbon-free energy source and a significant precursor to inorganic PM2.5 (hereafter PM2.5), aside from NOx and SOx. Since the emission of NH3 has often been overlooked compared to NOx and SOx, this study aims to reveal the role of NH3 and its emission control on PM2.5 in Kanto, Japan. With the aid of gas ratio (GR) quantitatively defining the stoichiometry between the three precursors to PM2.5, and the aid of atmospheric modeling software ADMER-PRO, coupled with thermodynamics calculations, the spatiotemporal distribution along with PM2.5 reduction under different NH3 emission cutoff strategies in Kanto had been revealed for the first time. The cutoff of NH3 emission could effectively reduce the PM2.5 concentration, with sources originated from agriculture, human/pet activities, and vehicle sources, overall giving a 93.32% PM2.5 reduction. Different cutoff strategies lead to distinct reduction efficiencies of the overall and local PM2.5 concentrations, with GR as a crucial factor. The regions with GR ∼1, are sensitive to the NH3 concentration for forming PM2.5, at which the NH3 reduction strategies should be applied with high priority. On the other hand, installing a new NH3 emission source should be avoided in the region with GR < 1, suppressing the so-yielded PM2.5 pollution. The future PM2.5 pollution control related to the NH3 emission control strategies based on GR, which is stoichiometry-based and applicable to regions other than Kanto, has been discussed.

Experimental analysis of PM2.5 reduction characteristics between Korean red pine (Pinus densiflora) and sawtooth oak (Quercus acutissima) saplings under different densities and arrangement structures.

As global air pollution, particularly fine particulate matter (PM2.5), has become a major environmental problem, various PM2.5 mitigation technologies including green infrastructure have received significant attention. However, owing to spatial constraints on urban greening, there is a lack of management plans for urban forests to efficiently mitigate PM2.5. In this study, we assessed the PM2.5 reduction capabilities of Pinus densiflora (Korean red pine) and Quercus acutissima (sawtooth oak) by measuring the changes of PM2.5 concentrations using an experimental chamber system. In addition, the PM2.5 reduction efficiency in 90 min (PMRE90) and the amount of PM2.5 reduction per leaf area (PMRLA) were compared based on arrangement structures and density levels. The results showed that the PM2.5 reduction by plants was significantly greater than that of the control experiment without any plants, and an additional reduction effect of approximately 1.38 times was induced by a 1.5 m s-1 air flow. The PMRE90 of Korean red pine was the highest at medium density. In contrast, the PMRE90 of sawtooth oak was the highest at high density. The PMRLA of both species was highest at low densities. The different responses of the species to total reduction were well explained by total leaf area (TLA). The PMRE90 of both species was positively correlated with TLA. The PMRLA of sawtooth oak was approximately 2.3 times greater than that of Korean red pine. However, there were no significant differences in both PMRE90 and PMRLA between the arrangement structures. Our findings reveal the potential mechanisms of vegetation in reducing PM2.5 according to arrangement structure and density. This highlights the importance of efficiently using urban green spaces with spatial constraints on PM2.5 mitigation in the future.

Comparisons of PM2.5 mitigation with stand characteristics between evergreen Korean pine plantations and deciduous broad-leaved forests in the Republic of Korea.

Owing to industrialization and urbanization in recent decades, fine particulate matter (PM2.5) in the atmosphere has become a major environmental problem worldwide. This environmental issue pushed the use of forests as air filtering tools. However, there is a lack of continuous and long-term forest management to efficiently mitigate PM2.5. In this study, we assessed the potential of different forest types to control air pollution by measuring the seasonal PM2.5 concentrations inside and outside the forest for one year. In addition, the PM2.5 reduction efficiencies (PMREs) of two forest types were compared, and their relationship with stand characteristics was analyzed. The results showed that the average PMRE inside the forests was approximately 18.2%; the seasonal PMRE was highest in winter (approximately 28.1%) and lowest in summer (approximately 9.6%). The average PMRE of the Taehwa deciduous broad-leaved forest (TDF) (approximately 18.8%) was significantly higher than that of the Taehwa coniferous forest (TCF) (approximately 17.5%) (P < 0.001); differences were also observed seasonally. The PMRE in the TCF was higher in spring and summer (P < 0.001), while that in the TDF was higher in autumn and winter (P < 0.001). Furthermore, the PMRE in the TDF was negatively correlated with stand density (P = 0.003) and positively correlated with the average diameter at breast height (DBH) (P = 0.028). However, the PMRE in the TCF did not significantly correlate with stand characteristics. As such, the results of this study revealed the differences in PM2.5 mitigation according to stand characteristics, which should be considered in urban forest management.

How to optimize provincial PM2.5 reduction targets and paths for emerging industrialized countries? Fresh evidence from China.

Taking China as an example, this paper aims to design a path to reduce haze pollution in newly industrialized countries. Combined the principles of efficiency and equity, this study develops a Game Cross Equity Fixed Cost Allocation Model (Game-EFCAM) to optimize provincial PM2.5 reduction targets and put forward haze reduction paths. The main results are as follows: (1) Among the three egalitarian principles, pay-ability egalitarianism is the fairest, which serves as the convergence principle of Game-EFCAM. (2) The "two mountains and two rivers" area has the largest PM2.5 emission quotas, while the provinces in western China have small PM2.5 emission quotas. In addition, the distributions of PM2.5 emission quotas per unit of population, per unit of gross domestic product, and per unit of area all show significant regional clustering. (3) Shandong and Hebei have the largest amount of PM2.5 emission reduction requirements, while Tianjin and Hainan have the smallest. Xinjiang and Beijing have the largest PM2.5 reduction pressure indexes, while Tianjin and Shanghai have the smallest. This study not only helps China optimize provincial PM2.5 reduction goals, but the proposed allocation method is also applicable to the overall planning of national resources utilization, and provides a reference to newly industrialized countries of haze reduction.

Relatively weak meteorological feedback effect on PM2.5 mass change in 2017/18 in the Beijing area: Observational evidence and machine-learning estimations.

Heavy aerosol pollution episodes (HPEs) in Beijing are worsened by the two-way feedback mechanism between unfavorable meteorological conditions and cumulative aerosols. In Winter 2017/18, mean PM2.5 mass concentration substantially decreased by 62% from 113 µg m-3 in Winter 2016/17 to 43 µg m-3. With reduced PM2.5 levels, the meteorological feedback on PM2.5 was relatively weak in Winter 2017/18. However, the weakening degree and its contributions to PM2.5 reduction are still uncertain. In this study, we investigated the change in the aerosol-induced modification of atmospheric stratification by combining PM2.5 data, radiosonde observations, and ERA-Interim reanalysis data, and then estimated the weakened meteorological feedback effect on PM2.5 change using machine learning. During polluted days, near-ground cooling bias, specific humidity (SH) increase, and relative humidity (RH) enhancement in Winter 2017/18 merely account for 38%, 65%, and 36% of the meteorological modification caused by aerosols in Winter 2016/17, respectively. Using machine learning algorithms with three most related variables, we found that during polluted days, the PM2.5 increase due to the meteorological feedback in Winter 2017/18 was merely 49% of that in Winter 2016/17. Effective pollution control and more favorable meteorological conditions have resulted in an additional benefit in PM2.5 reduction.