The implementation of effective waste separation policy is an important pathway to guide the public to actively participate in the waste separation action This study focused on exploring the Chinese public's response to the rigid and flexible waste separation policies from the perspectives of understanding, willingness to support, and willingness to implement We used a big data mining technique to obtain 10,057 entries of the Chinese public's response to the mandatory waste separation policy The results showed that "public's understanding-support willingness-implement willingness" regarding mandatory waste separation policy was characterized by a U-shaped response Specifically, the public's understanding and willingness to implement the rigid waste separation policy were relatively high in the short term, but their willingness to support this policy was relatively low and became increasingly low over time Particularly, "troublesome" implementation was deemed to the main reason for the public's low willingness to support the rigid waste separation policy In addition, we further obtained the sample data of the Chinese public regarding the flexible waste separation policy through the situational survey Contrary to the response characteristics of mandatory waste separation policy, the results showed that "public's understanding-support willingness-implement willingness" regarding flexible waste separation policy was characterized by an inverted U-shaped response, and the Chinese public showed more positive sentiment regarding the willingness to support and implement The results have important implications for guiding the public to actively participate in the waste separation action

Should Waste Separation Be Mandatory? A Study on Public's Response to the Policies in China.

The outbreak of the COVID-19 pandemic has emerged as a serious public health threat and has had a tremendous impact on all spheres of the environment. The air quality across the world improved because of COVID-19 lockdowns. Since the outbreak of COVID-19, large numbers of studies have been carried out on the impact of lockdowns on air quality around the world, but no studies have been carried out on the systematic review on the impact of lockdowns on air quality. This study aims to systematically assess the bibliographic review on the impact of lockdowns on air quality around the globe. A total of 237 studies were identified after rigorous review, and 144 studies met the criteria for the review. The literature was surveyed from Scopus, Google Scholar, PubMed, Web of Science, and the Google search engine. The results reveal that (i) most of the studies were carried out on Asia (about 65%), followed by Europe (18%), North America (6%), South America (5%), and Africa (3%); (ii) in the case of countries, the highest number of studies was performed on India (29%), followed by China (23%), the U.S. (5%), the UK (4%), and Italy; (iii) more than 60% of the studies included NO2 for study, followed by PM2.5 (about 50%), PM10, SO2, and CO; (iv) most of the studies were published by Science of the Total Environment (29%), followed by Aerosol and Air Quality Research (23%), Air Quality, Atmosphere & Health (9%), and Environmental Pollution (5%); (v) the studies reveal that there were significant improvements in air quality during lockdowns in comparison with previous time periods. Thus, this diversified study conducted on the impact of lockdowns on air quality will surely assist in identifying any gaps, as it outlines the insights of the current scientific research.

The Impact of COVID-19 Lockdowns on Air Quality—A Global Review

Rapid socio-economic development and urbanization have resulted in serious deterioration in air-quality in many world cities, including Beijing, China. This preliminary study is the first attempt to examine the effectiveness of air pollution control regulations implemented in Beijing during 2013 – 2017 through a data-driven regulatory intervention analysis. Our proposed machine-learning model utilizes proxy data including Aerosol Optical Depth (AOD) and meteorology; it can explain 80% of the PM 2.5 variability. Our preliminary results show that air pollution control regulatory measures introduced in China and Beijing have reduced PM 2.5 pollution in Beijing by 23% on average.

A Bayesian LSTM Model to Evaluate the Effects of Air Pollution Control Regulations in China

Mapping regional differences in payment for ecosystem service policies to inform integrated management: Case study of the Yangtze River Economic Belt.

The awareness and the engagement of various stakeholders play a crucial role in the successful implementation of climate policy and Sustainable Development Goals (SDGs). SDG 13, which refers to climate action, has three targets for combating climate change and its impact. Among the three targets, SDG 13.3 aims to “improve education, awareness-raising and human and institutional capacity on climate change mitigation, adaptation, impact reduction, and early warning”. This target should be implemented based on the understanding of climate change awareness among various groups of societies. Furthermore, the indicator related to awareness-raising is absent in SDG 13.3. Hence, this study aims to explore the differences in climate change awareness among various social groups within a country from a text mining technique. By collecting and analyzing a large volume of text data from various sources, climate change awareness was investigated from a multilateral perspective. Two text analyses were utilized for this purpose: Latent Dirichlet Allocation (LDA) topic modeling and term co-occurrence network analysis. In order to integrate and comparatively analyze the awareness differences among diverse groups, extracted topics were compared by classifying them into four indicators derived from the detailed targets in SDG 13.3: mitigation, adaptation, impact reduction, and early warning. The results show that the Korean public exhibited a relatively high awareness of early warning compared to the other four groups, and the media dealt with climate change issues with the widest perspective. The Korean government and academia notably had a high awareness of both climate change mitigation and adaptation. In addition, corporations based in Korea were observed to have substantially focused awareness on climate change mitigation for greenhouse gas reduction. This research successfully explored the disproportion and lack of climate change awareness formed in different societies of public, social, government, industry, and academic groups. Consequently, these results could be utilized as a decision criterion for society-tailored policy formulation and promoting climate action. Our results suggest that this methodology could be utilized as a new SDG indicator and to measure the differences in awareness.

Cross-Societal Analysis of Climate Change Awareness and Its Relation to SDG 13: A Knowledge Synthesis from Text Mining

Stakeholder concerns of air pollution in Hong Kong and policy implications: A big-data computational text analysis approach

This study investigates thoroughly whether acute exposure to outdoor PM2.5 concentration, P, modifies the rate of change in the daily number of COVID-19 infections (R) across 18 high infection provincial capitals in China, including Wuhan. A best-fit multiple linear regression model was constructed to model the relationship between P and R, from 1 January to 20 March 2020, after accounting for meteorology, net move-in mobility (NM), time trend (T), co-morbidity (CM), and the time-lag effects. Regression analysis shows that P (β = 0.4309, p < 0.001) is the most significant determinant of R. In addition, T (β = -0.3870, p < 0.001), absolute humidity (AH) (β = 0.2476, p = 0.002), P × AH (β = -0.2237, p < 0.001), and NM (β = 0.1383, p = 0.003) are more significant determinants of R, as compared to GDP per capita (β = 0.1115, p = 0.015) and CM (Asthma) (β = 0.1273, p = 0.005). A matching technique was adopted to demonstrate a possible causal relationship between P and R across 18 provincial capital cities. A 10 µg/m3 increase in P gives a 1.5% increase in R (p < 0.001). Interaction analysis also reveals that P × AH and R are negatively correlated (β = -0.2237, p < 0.001). Given that P exacerbates R, we recommend the installation of air purifiers and improved air ventilation to reduce the effect of P on R. Given the increasing observation that COVID-19 is airborne, measures that reduce P, plus mandatory masking that reduces the risks of COVID-19 associated with viral-particulate transmission, are strongly recommended. Our study is distinguished by the focus on the rate of change instead of the individual cases of COVID-19 when modelling the statistical relationship between R and P in China; causal instead of correlation analysis via the matching analysis, while taking into account the key confounders, and the individual plus the interaction effects of P and AH on R.

Outdoor PM2.5 concentration and rate of change in COVID-19 infection in provincial capital cities in China.

COVID-19 infection, first reported in Wuhan, China in December 2019, has become a global pandemic, causing significantly high infections and mortalities in Italy, the UK, the US, and other parts of the world. Based on the statistics reported by John Hopkins University, 4.7M people worldwide and 84,054 people in China have been confirmed positive and infected with COVID-19, as of 18 May 2020. Motivated by the previous studies which show that the exposures to air pollutants may increase the risk of influenza infection, our study examines if such exposures will also affect Covid-19 infection. To the best of our understanding, we are the first group in the world to rigorously explore the effects of outdoor air pollutant concentrations, meteorological conditions and their interactions, and lockdown interventions, on Covid-19 infection in China. Since the number of confirmed cases is likely to be under-reported due to the lack of testing capacity, the change in confirmed case definition, and the undiscovered and unreported asymptotic cases, we use the rate of change in the daily number of confirmed infection cases instead as our dependent variable. Even if the number of reported infections is under-reported, the rate of change will still accurately reflect the relative change in infection, provided that the trend of under-reporting remains the same. In addition, the rate of change in daily infection cases can be distorted by the government imposed public health interventions, including the lockdown policy, inter-city and intra-city mobility, and the change in testing capacity and case definition. Hence, the effects of the lockdown policy and the inter-city and intra-city mobility, and the change in testing capacity and case definition are all taken into account in our statistical modelling. Furthermore, we adopt the generalized linear regression models covering both the Negative Binomial Regression and the Poisson Regression. These two regression models, when combined with different time-lags (to reflect the COVID-19 incubation period and delay due to official confirmation) in air pollutant exposure (PM2.5), are used to fit the COVID-19 infection model. Our statistical study has shown that higher PM2.5 concentration is significantly correlated with a higher rate of change in the daily number of confirmed infection cases in Wuhan, China (p

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Shanshan Wang

Papers

Stakeholder concerns of air pollution in Hong Kong and policy implications: A big-data computational text analysis approach

Outdoor PM2.5 concentration and rate of change in COVID-19 infection in provincial capital cities in China.

Outdoor Air Pollutant Concentration and COVID-19 Infection in Wuhan, China

A Comparative Study of Air Pollution Trends in Historical London and Contemporary Beijing