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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".

Deadline for manuscript submissions: closed (31 August 2016) | Viewed by 70811

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Dr. Daniel A. Vallero

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Department of Civil & Environmental Engineering, Pratt School of Engineering, Duke University, Room 121 Hudson Hall, Box 90287, Durham, NC 27708-0287, USA
Interests: environmental engineering; air quality; policy, science and engineering; homeland security; aerosols; atmospheric science; waste, hazardous; ethics in engineering

Special Issue Information

Dear Colleagues,

Among the many changes in environmental science and engineering in recent decades, few have been more prominent than the transition from a nearly exclusive focus on pollution control to a comprehensive strategy to address the entire life cycle of a process that has led, or could lead to pollution. This Special Issue addresses this transition. Articles ranging from new sensor technologies for conventional and toxic air pollutants, to pollution prevention and waste minimization, or emerging applications of sustainability tools, such as life cycle analysis (LCA), quantitative structure activity relationships (QSARs), green chemistry (e.g. design for the environment [DfE]), and green engineering (e.g., design for disassembly [DfD]), as complements to ever-improving models, measurement techniques, and pollution control technologies. Articles address numerous, rapid changes in air quality sciences, including adverse outcome pathways, aggregate exposure pathways, precautionary principles, microenvironmental exposures, and the incorporation of social sciences, e.g., activities, e.g., product use, leading to exposure. Each article illustrates the change from treatment at the stack and vent to an air quality “systems” approach in the 21st century. This Special Issue is evidence of transitional science in air pollution monitoring, as it applies lessons learned and transfer of technologies from other fields, such as precision medicine, nanotechnologies, and computational toxicology. As such, it provides a snapshot of the state-of-the-science in air pollution measurements, modeling, controls, and prevention.

Dr. Daniel A. Vallero
Guest Editor

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Keywords

air pollution
ambient air quality
exposure science
exposure scenarios
exposure assessment
environmental risk
air pollutant measurement
air pollution modeling
green engineering
pollution prevention
life cycle analysis
sustainable design
design for the environment (DfE)
air toxics (National Emission Standards for Hazardous Air Pollutants - NESHAPS)
criteria pollutants (National Ambient Air Quality Standards - NAAQS)
emission testing
emission factors

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Published Papers (11 papers)

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Research

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2140 KiB

Open AccessArticle

Quick Green Scan: A Methodology for Improving Green Performance in Terms of Manufacturing Processes

by Aldona Kluczek

Sustainability 2017, 9(1), 88; https://doi.org/10.3390/su9010088 - 11 Jan 2017

Cited by 17 | Viewed by 7726

Abstract

The heating sector has begun implementing technologies and practices to tackle the environmental and social–economic problems caused by their production process. The purpose of this paper is to develop a methodology, “the Quick-Green-Scan”, that caters for the need of quick assessment decision-makers to improve green manufacturing performance in companies that produce heating devices. The study uses a structured approach that integrates Life Cycle Assessment-based indicators, framework and linguistic scales (fuzzy numbers) to evaluate the extent of greening of the enterprise. The evaluation criteria and indicators are closely related to the current state of technology, which can be improved. The proposed methodology has been created to answer the question whether a company acts on the opportunity to be green and whether these actions are contributing towards greening, maintaining the status quo or moving away from a green outcome. Results show that applying the proposed improvements in processes helps move the facility towards being a green enterprise. Moreover, the methodology, being particularly quick and simple, is a practical tool for benchmarking, not only in the heating industry, but also proves useful in providing comparisons for facility performance in other manufacturing sectors. Full article

(This article belongs to the Special Issue Air Pollution Monitoring and Sustainable Development)

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6633 KiB

Open AccessArticle

Initial Evaluation of Provincial-Level Environmental Risks from the Perspective of Human Settlements

by Shenzhen Tian, Xueming Li, Hang Li, Yingjia Zhang and Tongliga Bao

Sustainability 2016, 8(12), 1259; https://doi.org/10.3390/su8121259 - 2 Dec 2016

Cited by 4 | Viewed by 4352

Abstract

This study introduces risk theory of environmental science into human settlement science using 2004–2013 statistics, remote sensing data, and thematic maps. The entropy weight method and risk-index model are both used to study the characteristics of the time course and spatial pattern of human settlement risk in 31 provincial regions in China. In addition, influential mechanisms of vulnerability, functionality, stress, and adaptability on environmental risks are analyzed. Three primary results are obtained. First, for temporal characteristics, environmental risks of human settlements increased significantly from 2003 to 2012. The year 2006 marked both a sudden change and the cut-off point after which human settlements in China experienced qualitative changes and new risks. Second, for spatial characteristics, the risk index of human settlements decreased gradually from the southwestern to the northeastern, northwestern, and northern parts of China. The risk index of human settlement spaces differed significantly, with obvious block aggregation of spatial-distribution characteristics. Third, for relevant factor characteristics, between 2003 and 2012, the temporal change in vulnerability is relatively stable, with a slight increase in functionality and a slight decrease in adaptability. Spatially, Qinghai, Tibet, southwestern China, Guangdong, Guangxi, Beijing, and Tianjin had relatively high vulnerability in human settlements; Beijing, Tianjin, Jiangsu, and Zhejiang had the best functionality; Hunan and Sichuan had relatively high stress; and Guangdong, Jiangsu, and Zhejiang had relatively stronger adaptability. Further consideration and discussion are required on the environmental risks for different social groups and at different geographical scales, as well as on the uncertainty and long-term features of environmental risks in addition to environmental justice issues. Full article

(This article belongs to the Special Issue Air Pollution Monitoring and Sustainable Development)

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21 pages, 4652 KiB

Open AccessArticle

RETRACTED: Carbon Emission Mitigation Potentials of Different Policy Scenarios and Their Effects on International Aviation in the Korean Context

by Sungwook Yoon and Sukjae Jeong

Sustainability 2016, 8(11), 1179; https://doi.org/10.3390/su8111179 - 16 Nov 2016

Cited by 5 | Viewed by 5508 | Retraction

Abstract

The objective of this study is to seek better policy options for greenhouse gas (GHG) emission reduction in Korea’s international aviation industry by analyzing economic efficiency and environmental effectiveness with a system dynamics (SD) model. Accordingly, we measured airlines sales and CO₂ emission reductions to evaluate economic efficiency and environmental effectiveness, respectively, for various policies. The results show that the average carbon emission reduction rates of four policies compared to the business-as-usual (BAU) scenario between 2015 and 2030 are 4.00% (Voluntary Agreement), 7.25% (Emission Trading System or ETS-30,000), 8.33% (Carbon Tax or CT-37,500), and 8.48% (Emission Charge System or EC-30,000). The average rate of decrease in airline sales compared to BAU for the ETS policy is 0.1% at 2030. Our results show that the ETS approach is the most efficient of all the analyzed CO₂ reduction policies in economic terms, while the EC approach is the best policy to reduce GHG emissions. This study provides a foundation for devising effective response measures pertaining to GHG reduction and supports decision making on carbon tax and carbon credit pricing. Full article

(This article belongs to the Special Issue Air Pollution Monitoring and Sustainable Development)

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235 KiB

Open AccessArticle

Productivity Growth-Accounting for Undesirable Outputs and Its Influencing Factors: The Case of China

by Junfeng Zhang, Hong Fang, Bo Peng, Xu Wang and Siran Fang

Sustainability 2016, 8(11), 1166; https://doi.org/10.3390/su8111166 - 11 Nov 2016

Cited by 22 | Viewed by 4261

Abstract

Presently, China’s social development is facing the dilemma of supporting economic growth and reducing emissions. Therefore, it is crucial to analyse productivity growth and examine its relationship with influencing factors in China. This study evaluated the total factor productivity (TFP) growth of 30 provinces in China by adopting the Malmquist-Luenberger (ML) productivity index and incorporating undesirable outputs from 2011–2014. Then, a Tobit regression model was employed to explore the factors that influence China’s TFP growth. The results show that the average annual growth of the Malmquist-Luenberger productivity index was lower than that of the traditional Malmquist (M) productivity index growth during the research period. The findings reveal several key conclusions: First, the true TFP growth in China will be overestimated if undesirable outputs are ignored. Second, technical changes are the main contributor to TFP growth. Third, there are huge regional disparities of productivity growth in China. Fourth, coal intensity, environmental regulations, and industrial structure have significantly negative effects on productivity growth, while real per capita gross domestic product (GDP) and foreign direct investment (FDI) have strongly positive effects on productivity growth. Full article

(This article belongs to the Special Issue Air Pollution Monitoring and Sustainable Development)

7012 KiB

Open AccessArticle

Estimating Air Particulate Matter Using MODIS Data and Analyzing Its Spatial and Temporal Pattern over the Yangtze Delta Region

by Jianhui Xu, Hong Jiang, Zhongyong Xiao, Bin Wang, Jian Wu and Xin Lv

Sustainability 2016, 8(9), 932; https://doi.org/10.3390/su8090932 - 13 Sep 2016

Cited by 6 | Viewed by 4690

Abstract

The deteriorating air quality in the Yangtze delta region is attracting growing public concern. In this paper, seasonal estimation models of the surface particulate matter (PM) were established by using aerosol optical thickness (AOT) retrievals from the moderate resolution imaging spectro-radiometer (MODIS) on board NASA’s Terra satellite. The change of the regional distribution of the atmospheric mixed layer, relative humidity and meteorological elements have been taken into account in these models. We also used PM mass concentrations of ground measurements to evaluate the estimation accuracy of those models. The results show that model estimation of PM_2.5 and PM₁₀ mass concentrations were in good agreement with the ground-based observation of PM mass concentrations (p < 0.01, the R² value of the PM_2.5 concentrations experimental model for four seasons are 0.48, 0.62, 0.61 and 0.52 respectively. The R² value of PM₁₀ concentrations experimental model for four seasons are 0.57, 0.56, 0.64 and 0.68 respectively). At the same time, spatial and temporal variations of PM_2.5 and PM₁₀ mass concentrations were analysed over the Yangtze delta region from 2000 to 2013. The results show that PM_2.5 and PM₁₀ show a trend of increase in the Yangtze delta region from 2000 to 2013 and change periodically. The maximum mass concentration of PM_2.5 and PM₁₀ was in January–February, and the minimum was in July–August. The highest values of PM_2.5 and PM₁₀ mass concentration are in the region of urban agglomeration which is grouped to a delta-shaped region by Shanghai, Hangzhou and Nanjing, while the low values are in the forest far away from the city. PM mass concentration over main cities and rural areas increased gradually year by year, and were increasing more quickly in urban areas than in rural areas. Full article

(This article belongs to the Special Issue Air Pollution Monitoring and Sustainable Development)

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1098 KiB

Open AccessArticle

Predicting the Evolution of CO₂ Emissions in Bahrain with Automated Forecasting Methods

by Cristiana Tudor

Sustainability 2016, 8(9), 923; https://doi.org/10.3390/su8090923 - 9 Sep 2016

Cited by 20 | Viewed by 8679

Abstract

The 2012 Doha meeting established the continuation of the Kyoto protocol, the legally-binding global agreement under which signatory countries had agreed to reduce their carbon emissions. Contrary to this assumed obligation, all G20 countries with the exception of France and the UK saw significant increases in their CO₂ emissions over the last 25 years, surpassing 300% in the case of China. This paper attempts to forecast the evolution of carbon dioxide emissions in Bahrain over the 2012–2021 decade by employing seven Automated Forecasting Methods, including the exponential smoothing state space model (ETS), the Holt–Winters Model, the BATS/TBATS model, ARIMA, the structural time series model (STS), the naive model, and the neural network time series forecasting method (NNAR). Results indicate a reversal of the current decreasing trend of pollution in the country, with a point estimate of 2309 metric tons per capita at the end of 2020 and 2317 at the end of 2021, as compared to the 1934 level achieved in 2010. The country’s baseline level corresponding to year 1990 (as specified by the Doha amendment of the Kyoto protocol) is approximately 25.54 metric tons per capita, which implies a maximum level of 20.96 metric tons per capita for the year 2020 (corresponding to a decrease of 18% relative to the baseline level) in order for Bahrain to comply with the protocol. Our results therefore suggest that Bahrain cannot meet its assumed target. Full article

(This article belongs to the Special Issue Air Pollution Monitoring and Sustainable Development)

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564 KiB

Open AccessArticle

Effects of the Post-Olympics Driving Restrictions on Air Quality in Beijing

by Hua Ma and Guizhen He

Sustainability 2016, 8(9), 902; https://doi.org/10.3390/su8090902 - 6 Sep 2016

Cited by 27 | Viewed by 6840

Abstract

To reduce congestion and air pollution, 20% driving restriction, a license plate-based traffic control measure, has been implemented in Beijing since October 2008. While the long-term impacts of this policy remain controversial, it is important to understand how and why the policy effects of driving restrictions change over time. In this paper, the short- and long-run effects of the 20% driving restrictions in Beijing and the key factors shaping the effects are analyzed using daily PM₁₀ pollution data. The results showed that in the short run, 20% driving restriction could effectively reduce ambient PM₁₀ levels. However, this positive effect rapidly faded away within a year due to long-term behavioral responses of residents. A modified 20% restriction, designed to replace the original 20% restriction system since April 2009, which is less stringent and provides more possibility for intertemporal driving substitution, has shown some positive influence on air quality over the long run comparing with that under the original policy design. Temporarily, the more stringent the driving restriction was, the better effects it would have on air quality. In the long-run, however, the policy was likely to cause a vicious circle, and more stringent policy might induce stronger negative incentives which would result in even worse policy effects. Lessons learned from study of the effects of driving restrictions in Beijing will help other major cities in China and abroad to use driving restrictions more prudently and effectively in the future. Decision-makers should carefully consider the pros and cons of a transport policy and conduct the ex-ante and ex-post evaluations on it. Full article

(This article belongs to the Special Issue Air Pollution Monitoring and Sustainable Development)

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889 KiB

Open AccessArticle

Perception of Cabin Air Quality among Drivers and Passengers

by Doru Constantin, Crisanta-Alina Mazilescu, Mihai Nagi, Anca Draghici and Ana-Andreea Mihartescu

Sustainability 2016, 8(9), 852; https://doi.org/10.3390/su8090852 - 29 Aug 2016

Cited by 11 | Viewed by 7345

Abstract

Air analysis inside vehicles is a problem that can be interpreted from several perspectives. This research is oriented towards the perception of air quality within a car, regarding a situation of cars in stationary traffic. Carbon dioxide measurements were made using a Trotec Data Logger Air Quality CO₂ BZ30 machine inside different standing vehicles with up to five occupants, with and without circulating air. The perception of the air quality was measured on a Likert-type scale with seven levels on a sample group of 60 students. The results highlight, on the one hand, the conditions under which the CO₂ in the cabin air can reach concentrations which are, according to new data, considered to influence the cognitive capacity of occupants in the car, and on the other hand, they present a global assessment of the air quality in the vehicle when critical values of CO₂ have been reached. If the air exchange rates inside a car are low, this degrades the air quality in such a way that it affects the concentration and reactions necessary for safe driving without perceiving any discomfort that would put the drivers or the passengers on alert. Full article

(This article belongs to the Special Issue Air Pollution Monitoring and Sustainable Development)

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2829 KiB

Open AccessArticle

Self-Adaptive Revised Land Use Regression Models for Estimating PM_2.5 Concentrations in Beijing, China

by Lujin Hu, Jiping Liu and Zongyi He

Sustainability 2016, 8(8), 786; https://doi.org/10.3390/su8080786 - 11 Aug 2016

Cited by 23 | Viewed by 6471

Abstract

Heavy air pollution, especially fine particulate matter (PM_2.5), poses serious challenges to environmental sustainability in Beijing. Epidemiological studies and the identification of measures for preventing serious air pollution both require accurate PM_2.5 spatial distribution data. Land use regression (LUR) models are promising for estimating the spatial distribution of PM_2.5 at a high spatial resolution. However, typical LUR models have a limited sampling point explanation rate (SPER, i.e., the rate of the sampling points with reasonable predicted concentrations to the total number of sampling points) and accuracy. Hence, self-adaptive revised LUR models are proposed in this paper for improving the SPER and accuracy of typical LUR models. The self-adaptive revised LUR model combines a typical LUR model with self-adaptive LUR model groups. The typical LUR model was used to estimate the PM_2.5 concentrations, and the self-adaptive LUR model groups were constructed for all of the sampling points removed from the typical LUR model because they were beyond the prediction data range, which was from 60% of the minimum observation to 120% of the maximum observation. The final results were analyzed using three methods, including an accuracy analysis, and were compared with typical LUR model results and the spatial variations in Beijing. The accuracy satisfied the demands of the analysis, and the accuracies at the different monitoring sites indicated spatial variations in the accuracy of the self-adaptive revised LUR model. The accuracy was high in the central area and low in suburban areas. The comparison analysis showed that the self-adaptive LUR model increased the SPER from 75% to 90% and increased the accuracy (based on the root-mean-square error) from 20.643 μg/m³ to 17.443 μg/m³ for the PM_2.5 concentrations during the winter of 2014 in Beijing. The spatial variation analysis for Beijing showed that the PM_2.5 concentrations were low in the north, especially in the northwest region, and high in the southern and central portions of Beijing. This spatial variation was consistent with the fact that the northern region is mountainous and has fewer people and less traffic, which results in lower air pollution, than in the central region, which has a high population density and heavy traffic. Moreover, the southern region is adjacent to Hebei province, which contains many polluting enterprises; thus, this area exhibits higher air pollution levels than Beijing. Therefore, the self-adaptive revised LUR model is effective and reliable. Full article

(This article belongs to the Special Issue Air Pollution Monitoring and Sustainable Development)

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1398 KiB

Open AccessArticle

How to Move China toward a Green-Energy Economy: From a Sector Perspective

by Jie-fang Dong, Qiang Wang, Chun Deng, Xing-min Wang and Xiao-lei Zhang

Sustainability 2016, 8(4), 337; https://doi.org/10.3390/su8040337 - 6 Apr 2016

Cited by 25 | Viewed by 6774

Abstract

With China’s rapid economic growth, energy-related CO₂ emissions have experienced a dramatic increase. Quantification of energy-related CO₂ emissions that occur in China is of serious concern for the policy makers to make efficient environmental policies without damaging the economic growth. Examining 33 productive sectors in China, this paper combined the extended “Kaya identity” and “IPAT model” with the Log-Mean Divisia Index Method (LMDI) to analyze the contribution of various factors driving of energy-related CO₂ emissions in China during 1995–2009. Empirical results show that the main obstacle that hinders China’s transition to a green energy economy is the economic structure characterized by high carbon emissions. In contrast, the increased proportion of renewable energy sources (RES) and the improvement of energy efficiency play a more important role in reducing carbon emissions. Moreover, the power sector has a pivotal position in CO₂ emissions reduction, primarily because of the expansion of electricity consumption. These findings suggest that policies and measures should be considered for various industrial sectors to maximize the energy efficiency potential. In addition, optimizing the industrial structure is more urgent than adjusting the energy structure for China. Full article

(This article belongs to the Special Issue Air Pollution Monitoring and Sustainable Development)

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Review

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1568 KiB

Open AccessReview

Air Pollution Monitoring Changes to Accompany the Transition from a Control to a Systems Focus

by Daniel A. Vallero

Sustainability 2016, 8(12), 1216; https://doi.org/10.3390/su8121216 - 24 Nov 2016

Cited by 18 | Viewed by 6123

Abstract

During the 20th century, air pollution control technologies grew at an amazingly rapid rate. Air quality in much of the industrialized world greatly improved as the efficiencies of these technologies improved. This continued improvement in pollution control has more recently been complemented with measures to prevent the emission of air pollutants. The previous, exclusive focus on treatment requires systems thinking. This review provides a framework for this Special Issue of Sustainability by describing the new tools that are needed to support this new, broader focus, including life cycle assessments, exposure models, and sustainable design. Full article

(This article belongs to the Special Issue Air Pollution Monitoring and Sustainable Development)

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