Abstract

Introduction

Material and methods

Results and discussion

Conclusions

Acknowledgments

Supplementary materials

Reference

Abstract

     Air pollution is a major obstacle to future sustainability, and traffic pollution has become a large drag on the sustainable developments of future metropolises. Here, combined with the large volume of real-time monitoring data, we propose a deep learning model, iDeepAir, to predict surface-level PM2.5 concentration in Shanghai megacity and link with MEIC emission inventory creatively to decipher urban traffic impacts on air quality. Our model exhibits high-fidelity in reproducing pollutant concentrations and reduces the MAE from 25.355 μg/m3 to 12.283 μg/m3 compared with other models. And identifies the ranking of major factors, local meteorological conditions have become a nonnegligible factor. Layer-wise relevance propagation (LRP) is used here to enhance the interpretability of the model and we visualize and analyze the reasons for the different correlation between traffic density and PM2.5 concentration in various regions of Shanghai. Meanwhile, As the strict and effective industrial emission reduction measurements implementing in China, the contribution of urban traffic to PM2.5 formation calculated by combining MEIC emission inventory and LRP is gradually increasing from 18.03% in 2011 to 24.37% in 2017 in Shanghai, and the impact of traffic emissions would be ever-prominent in 2030 according to our prediction.

Introduction

     Air pollution is a large obstacle to the world’s future sustainable developments, and millions of people die from air pollution-related diseases every year around the world (Zheng et al., 2017). This is seriously severe in some developing countries like China (Lelieveld et al., 2015), which has the highest country-level values globally for the population-weighted annual average concentration of PM2.5 (Tichenor and Sridhar, 2019; Zhang et al., 2012) and has been a major public health concern in recent years (Li et al., 2019a) . Shanghai, one of the most developed and populous cities in China, has suffered severe increasing haze episodes mostly attributed to the severe particle pollution especially fine particles (particles ≤2.5μm in aerodynamic diameter; PM2.5)(Han et al., 2020) since 1990s with the rapid urbanization and industrialization (Wang et al., 2015) . Under these circumstances, air pollution-related diseases have emerged gradually, such as respiratory diseases in the elderly and preterm birth and low birth weight for birth when maternal exposure to PM2.5 in Shanghai(Li et al., 2019a; Liu et al., 2017) .

Conclusions

     In this article, we propose a novel method to quantify the influence of anthropogenic emissions on PM2.5 concentration with a novel and traceable deep learning model. Our experiment results indicate that the proposed model could achieve better fitting and prediction performances than the LSTM, GBRT, Seq2Seq, DA-RNN model and other deep learning models. Furthermore, we output the contributions of input parameter to the prediction in LRP method and visualize and analyze the spatial correlation between traffic flow and PM2.5.

     In addition, we discover that transportation emissions will play the most dominate role in future urban air pollution, and how to reduce traffic emissions are an unavoidable issue on achieving sustainable developments in modern cities. Meanwhile, to some extent, new energy vehicles can be considered as an effective way to reduce traffic emissions. However, the current promotion policies and efforts are far from enough. To further improve urban air quality, we need some more effective and powerful measurements in response to the rapid growth of urban traffic emissions.

Highlights

Abstract

Graphical abstract

Keywords

1. Introduction

2. Materials and methods

3. Results and discussions

4. Conclusion

Author contribution

Declaration of competing interest

Acknowledgement

Appendix A. Supplementary data

References

Abstract

During the Covid-19 pandemic, personal protection equipment (PPE) was widely used to control the virus further spared. In this study, the presence of PPE wastes along the coastline of Bushehr port, the Persian Gulf from nine stations was investigated (4 times during 40 days), and their potential for microplastics (MPs) creation was preliminarily assessed. In total, more than 2380 PPE were collected in the study area. No significant differences were found between various beaches regarding their types and common activities. In addition, the estimated disposal rate of PPE per day and year is 350 and 127,750 items, respectively. More than 10% of the collected PPE from Bushehr’s coastal areas on each sampling day were damaged. Based on the microscopic analysis, the left surgical masks and torn plastic gloves in the coastal regions are emerging sources of secondary microfibers and MP particles (mostly fragments and films) in the marine environments, respectively.

1. Introduction

During the last year, human beings have faced deadly infectious disease, and since March 2020, Covid-19 was declared as a pandemic by the World Health Organization (WHO) (Ducharme, 2020). Then, different measures have been used in different parts of the world, such as staying at home (lockdown), travel restriction, social distancing, washing of hands, surface disinfection, and isolation (Aragaw, 2020; Fadare and Okoffo, 2020; Rhee, 2020). Moreover, various personal protective equipment (PPE-i.e., face masks, gloves, face shields, alcohol spray) was used in different countries in order to control the virus further spared (De-la-Torre and Aragaw, 2021; Nowakowski et al., 2020). It is estimated that more than 129 billion face masks and 65 billion gloves have been used monthly all around the world (Prata et al., 2020). Discarded only 1% of these PPE exceeds ten million items (Celis et al., 2021) and can cause an environmental crisis.

Single-use face masks, N95 masks, gloves, and face shields are mainly produced from plastic polymers such as polypropylene, polystyrene, polyacrylonitrile, polyester, and polyurethane (Aragaw, 2020; Ardusso et al., 2021; Potluri and Needham, 2005; Sangkham, 2020). Most of the surgical masks have three layers: 1) an inner layer with soft fibers, 2) a middle layer with melt-gusted filter, and 3) an outer layer with nonwoven fibers which are usually colored and water-resistant (Aragaw, 2020; Fadare and Okoffo, 2020). Approximately the weight of polypropylene (PP) in a single N95 mask and surgical mask is 11 and 4.5 g, respectively (Abbasi et al., 2020; Liebsch, 2020). In addition, the most commonly single-use gloves are made of polyethylene (PE), latex, and nitrile (Nowakowski et al., 2020). Although these types of plastics can easily be recycled, they are usually mixed with other wastes or left in the environment (i.e., parks, beaches, forests) (Nowakowski et al., 2020).

Highlights

Abstract

Keywords

1. Introduction

2. Methodology

3. Results and discussion

CRediT authorship contribution statement

Declaration of competing interest

Acknowledgement

Appendix A. Supplementary data

Disclaimer

Research Data

References

ABSTRACT

We implement oceanic dimethylsulfide (DMS) emissions and its atmospheric chemical reactions into the Community Multiscale Air Quality (CMAQv53) model and perform annual simulations without and with DMS chemistry to quantify its impact on tropospheric composition and air quality over the Northern Hemisphere. DMS chemistry enhances both sulfur dioxide (SO2) and sulfate (SO2− 4 ) over seawater and coastal areas. It enhances annual mean surface SO2 concentration by +46 pptv and SO2− 4 by +0.33 μg/m3 and decreases aerosol nitrate concentration by − 0.07 μg/m3 over seawater compared to the simulation without DMS chemistry. The changes decrease with altitude and are limited to the lower atmosphere. Impacts of DMS chemistry on SO2− 4 are largest in the summer and lowest in the fall due to the seasonality of DMS emissions, atmospheric photochemistry and resultant oxidant levels. Hydroxyl and nitrate radical-initiated pathways oxidize 75% of the DMS while halogeninitiated pathways oxidize 25%. DMS chemistry leads to more acidic particles over seawater by decreasing aerosol pH. Increased SO2− 4 from DMS enhances atmospheric extinction while lower aerosol nitrate reduces the extinction so that the net effect of DMS chemistry on visibility tends to remain unchanged over most of the seawater.

1. Introduction

In the 30+ years since Charlson et al. (1987) hypothesized that biogenically-produced dimethyl sulfide (DMS) from marine phytoplankton participates in a negative climate feedback loop affecting cloud condensation nuclei and cloudiness, the study of DMS from the world’s oceans has been a vigorous area of research. Though the CLAW hypothesis (named after the authors of Charlson et al., 1987) has been criticized as too simplistic (Quinn and Bates, 2011), the resulting knowledge gained about the sources, oceanic concentrations, and emissions of oceanic DMS has enabled chemical transport and earth systems models to realistically simulate its impacts on air quality and climate. DMS in the ocean is produced from the breakdown of dimethylsulfoniopropionate (DMSP) generated from microalgal metabolic processes and exudation/mortality (Stefels et al., 2007). The concentration of DMS in seawater has been sampled extensively, leading to the construction of the Global Surface Seawater DMS Database (http://saga.pmel.noaa.gov/dms) and interpolated estimates of the global concentration distribution (Kettle et al., 1999; Kettle and Andreae, 2000). An updated climatology of oceanic DMS concentrations using over 47,000 measurements was reported by Lana et al. (2011).

1- Introduction

2- Unfolding China’s pollutant emissions trading practices

3- Regional comparison of PETSs

4- Discussion

5- Conclusion

References

Abstract

Over the past few decades, the pollutant emissions trading policies in China have undergone significant innovation and exploration. It is considered as a market-based approach that became integrated with command-and-control mechanisms such as total emissions control or pollution permits. This study is the first to provide systematic, reflective thinking that tracks the regional initiatives of pollutant emissions trading systems in China. In this article, we divided China’s emissions trading practices into three stages and conducted a comparative qualitative analysis of the country’s eleven provincial emissions trading pilots. We found that provincial pilots are highly diverse and complex regarding the pollutants that can be traded, the industrial sectors involved, the design of trading administration and processes, and the implementation of trading practices such as allowance, pricing and platforms. We also identified four main challenges: legislation setup, monitoring and verification, administrative interference, and the technical quantification of pollutant hotspots. We conclude the article by providing policy implications so that emissions trading policies can be integrated with the newly developed pollution permitting system.

Introduction

The economic incentive approach enables more flexibility and efficiency in environmental management. Emissions trading has been recognized as one of the most effective environmental policies in Western countries (Stavins, 1995; FØRSUND and NÆVDAL, 1997). In the United States, for example, the report on emissions trading policies issued by the Regan Government summarized the practices of banking, bubbles, offsets, and tradable credit (Schwarze and Zapfel, 2000). These practices were integrated into an emissions trading system at the state level, which formalized the market mechanisms to monitor and reduce pollution. The EU Emission Trading System (EU-EST) covers approximately 11 000 power stations and manufacturing plants in the 28 EU Member States, as well as aviation activities in these countries (Boemare and Quirion, 2002). In total, roughly 45% of total EU greenhouse gas emissions are regulated by the EU-ETS.1 China has been piloting pollutant emissions trading systems (PETSs) since the 1980s. Despite the complexities and ambiguities of policy design and implementation, PETS initiatives keep evolving with ongoing challenges of environmental pollution and have become one of the fundamental environmental policies in China for point-source environmental management (Jiang et al., 2016; Guo, 2018). Thus, such initiatives are significantly different from carbon (CO2) emissions trading systems in China (Jotzo and Loschel, € 2014; B. Zhang et al., 2014) since the latter are usually not considered part of China’s fundamental climate mitigation strategies. Most previous studies about PETSs in China apply environmental economics analyses (Cao and Ikeda, 2005; Zhang et al., 2010) to reveal the efficiency of a single emissions trading scheme at the enterprise level, regardless of the regulatory nature of a single pollutant, i.e., CO2 is not considered ak “pollutant” in China at central or local levels. This is probably why the outcomes of PETSs are diverse (Ji et al., 2017; Dickson and Mackenzie, 2018; Xian et al., 2019). Researchers have also pointed out that the maneuverability of the current emissions trading policy, conflicts between different environmental policies, and administrative interference are considered as the main barriers to the success of the SO2 emissions trading programs in China (Wang et al., 2002; Dudek et al. 2004, 2007).

1- Introduction

2- Wind-coal combined power generation system

3- Data and results

4- Conclusion

References

Abstract

China’s power supply structure is dominated by coal-fired power. As China’s renewable power expands, coal-fired power units are required to improve flexibility to balance power system. However, more flexible operation of coal power units increases energy consumption and pollutant emissions. This paper examines the energy efficiency, CO2 and pollutant emissions characteristics of China’s generic wind-coal combined power generation system, and discusses pollution-minimizing dispatch strategies by modeling three hypothetical scenarios of wind-coal combined power generation systems. For day-scale analyses, we find expected displacement rates of coal consumption and CO2 emission decrease by wind fraction rate, indicating reduced energy savings from wind power per unit of increase in wind capacity. For NOx emission, we find that expected displacement rate reaches maximum value of 112% when wind fraction rate is equal to 0.27.For week-scale analysis, we simulate the coal consumption rate and emission factors with and without an operation of shutdown and startup of coal power units in the system. We suggest future study on dispatch decisions of startup/shutdown of coal power units to optimize power generation economically and environmentally.

Introduction

China has proposed an ambitious development plan for renewable energy (NDRC and SERC, 2011; NDRC and NEA, 2016; NEA and NDRC, 2017; Yuan, 2016a, b. The goal is to achieve 15% non-fossil energy supply by 2020 and 20% non-fossil energy supply by 2030, and the goal will mainly be achieved via expansion of wind and solar energy (Yuan et al., 2007, Yuan and Xu 2014a,b; Yuan, 2016a, b; Yuan and Na, 2016; Yuan and Lei, 2016; among others).With strong support of national policies, renewable power generation has experienced rapid development in the past decade (CEC, 2018). By the end of 2016, China has become the world’s largest investor of wind and solar power capacity. Due to intermittency of wind and solar power generation, large-scale wind and solar integration onto power grid inevitably requires other generation sources to balance demand load (Denny and O’Malley, 2006; Valentino and Valenzuela, 2012; Lu et al., 2011; Lu et al., 2014; Liu et al., 2015; Na et al., 2018). China’s power supply structure is still dominated by coal-fired power, and the more flexible power sources such as gas turbine power generation and pumped-storage power generation account for only a small share. Many existing studies have studied the CO2 emissions scenarios in China’s energy and power system, but few works have addressed the impact of renewable integration on CO2emissions in the power system (Chen and Cai, 2017; Su and Fang, 2016; Liu et al., 2017; Peng and Xie, 2018; Zhou and Wang, 2018; Ye and Ren, 2018; Tang et al., 2018). In the three northern regions of China (North China, Northwest China, Northeast China), wind and solar power generation account for a larger proportion, and the system load is mainly balanced by coal-fired power units. Although wind and solar power can be considered to produce zero emissions, their intermittent and uncertainties can impose negative impacts on economics and emissions of peaking units in the system.

1- Introduction

2- The evolutionary game model

3- System dynamics model for an evolutionary game

4- Conclusions and limitations

References

Abstract

Governments and consumers are paying more attention to environmental protection. China, Korea, and several European countries have implemented market-based cap-and-trade systems to reduce carbon emissions. At the same time, consumers are willing to pay more for low-carbon products. The decisions of manufacturers and retailers may be impacted by these factors. This paper considers a scenario with a model economy under the effects of a cap-and-trade policy, with consumers who prefer low-carbon products, and develops an evolutionary game (EG) model to examine the evolution of behaviors for powerful retailers (such as Amazon, Gome, Walmart, etc.) and manufacturers in a retailer-led supply chain. In such a supply chain, the retailers can choose whether or not to promote low-carbon products and manufacturers can choose whether or not to reduce carbon emissions. A Stackelberg game structure is used to identify the optimal decisions for manufacturers and retailers. A model is developed to investigate the stability of the equilibrium solutions of the evolutionary game. System dynamics is used to simulate and analyze dynamic and transient behaviors, and is used to simulate the evolutionary game in a Chinese appliance industry. The simulation results show that the emissions cap, the market price of carbon credits, and the consumers’ preferences for low-carbon products are key factors influencing the retailers’ and manufacturers’ behavior. To increase long-term profits for both retailers and manufacturers, the retailers and the manufacturers should make sustainable decisions in tandem.

Introduction

With the rapid development of industrial capabilities in China and other parts of the world, more greenhouse gasses (GHGs) have been emitted due to industrial production processes, which damage the environment. After implementing the Kyoto Protocol in 1997 and the Paris Climate Agreement in 2015, Europe, China, and Korea have attempted to enact a variety of policies and legislation to reduce carbon emissions (Goulder and Schen, 2013; Zhang and Xu, 2013). For example, in China, the National Development and Reform Commission (NDRC) instructed Beijing, Shanghai, Guangdong, and four other cities to implement a carbon emissions trading mechanism. According to NDRC statistics, between the implementation of the program and September 2017, 197 million tons and more than 4.5 billion RMB of carbon credits were exchanged in these seven cities. Under this cap-and-trade system, the government allocates a free limit on carbon emissions to an individual enterprise. If an enterprise produces a larger amount of carbon emissions than the emissions cap, it has to buy credits for the extra carbon emissions; otherwise, it can earn additional revenue by selling the unused carbon credits at the market price (Benjaafar et al., 2013). For example, in 2013, Foxconn invested less than 50 million RMB in energy-saving retrofits but gained 10 million RMB in profit (an increase of 60 million RMB in revenue) by selling the surplus carbon credits; this set of transactions accounted for nearly 30% of the annual surplus credits in Shenzhen. Therefore, the carbon trading market has created a new cost mechanism (Alhaj et al., 2016) for enterprises and could influence the enterprises’ production planning and ordering strategies (Cheng et al., 2017; Drake et al., 2010).

Distillery industries (DIs) are the key contributors to the world’s economy, but these industries also considered as one of the major source of environmental pollution worldwide. In India, there are ~ 319 distilleries with annual production of 3.25 × 109 L of alcohol and 40.4 × 1010 L of wastewater (Chandra et al., 2012; Uppal, 2004). The global production of bioethanol in 2007 was 50 billion liters whereas, in 2008, it reached 60 billion liters representing almost 4% of the 62 world’s gasoline consumption (Mussatto et al., 2010; Balat and Balat, 2009). DIs generate a huge volume of dark colored wastewater characterized by its dark brown color, acidic pH (5.4-4.5), high BOD (40,000- 50,000 mg/L), COD (80,000-100,000 mg/L), total dissolved solids (TDS), total solids (TS), total suspended solid (TSS), with high nitrogen, potassium, phosphates, calcium, and sulfate content (Table 1). The high BOD and COD values of DWW are mainly due to the presence of high organic content such as proteins, reduced sugars, polysaccharides, lignin, melanoidins, and waxes along with a complex mixture of recalcitrant organic pollutants (Chowdhary et al., 2017). In distillery wastewater (DWW), melanoidins are the major coloring pollutants causing serious environmental problems and health threats in human and animals (Tamanna and Mahmood, 2015; Saranraj and Dtella, 2014). Melanoidins are recalcitrant compounds of sugar and amino acids, formed during the processing of sugar cane juice in sugar factories and molasses in distillery industries (Arimi et al., 2015; Saranraj and Dtella, 2014; Onyango et al., 2012; Wang et al., 2011; Agarwal et al., 2010; Mohana et al., 2009; Plavsic et al., 2006).