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Ducruet C, Polo Martin B, Sene MA, Lo Prete M, Sun L, Itoh H, Pigné Y. Ports and their influence on local air pollution and public health: A global analysis. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 915:170099. [PMID: 38224889 DOI: 10.1016/j.scitotenv.2024.170099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 01/04/2024] [Accepted: 01/09/2024] [Indexed: 01/17/2024]
Abstract
Despite the skyrocketing growth in recent decades of environmental studies on ports and shipping, their local health impacts remain largely under-researched. This article tackles this gap in research by statistically analyzing data on global shipping flows across nearly 5000 ports in 35 OECD countries between 2001 and 2018. The different traffic types, from containers to bulk and passengers, are analyzed jointly with data on natural conditions, air pollution, socio-economic indicators, and public health. The principal results show that port regions pollute more than non-port regions on average, while health impacts vary according to the size and specialization of the port region. Three types of port regions are clearly differentiated: industrial, intermediate, and metropolitan port regions.
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Affiliation(s)
- César Ducruet
- French National Centre for Scientific Research, UMR 7235 EconomiX, University of Paris-Nanterre, France.
| | - Bárbara Polo Martin
- French National Centre for Scientific Research, UMR 7235 EconomiX, University of Paris-Nanterre, France
| | - Mame Astou Sene
- French National Centre for Scientific Research, UMR 7235 EconomiX, University of Paris-Nanterre, France
| | - Mariantonia Lo Prete
- Laboratory Territoires, Villes, Environnement et Société (TVES ULR 4477), Université du Littoral Côte d'Opale (ULCO), France
| | - Ling Sun
- Fudan University & Shanghai Maritime University, China
| | | | - Yoann Pigné
- LITIS, University of Le Havre Normandie, France
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Carbon Emission Measurement of Urban Green Passenger Transport: A Case Study of Qingdao. SUSTAINABILITY 2022. [DOI: 10.3390/su14159588] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Urban passenger transport is one of the most significant sources of fossil energy consumption and greenhouse gas emission, especially in developing countries. The rapid growth of urban transport makes it a critical target for carbon reduction. This paper establishes a method for calculating carbon emission from urban passenger transport including ground buses, private cars, cruising taxis, online-hailing taxis, and rail transit. The scope of the study is determined according to the transportation mode and energy type, and the carbon emission factor of each energy source is also determined according to the local energy structure, etc. Taking into consideration the development trend of new energy vehicles, a combination of “top-down” and “bottom-up” approaches is used to estimate the carbon dioxide emission of each transportation mode. The results reveal that carbon emission from Qingdao’s passenger transport in 2020 was 8.15 million tons, of which 84.31% came from private cars, while the share of private cars of total travel was only 45.66%. Ground buses are the most efficient mode of transport. Fossil fuels emit more greenhouse gases than other clean energy sources. The emission intensity of hydrogen fuel cell buses is better than that of other fuel type vehicles. Battery electric buses have the largest sensitivity coefficient, therefore the carbon emission reduction potentially achieved by developing battery electric buses is most significant.
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Kang Y, Choi H, Im J, Park S, Shin M, Song CK, Kim S. Estimation of surface-level NO 2 and O 3 concentrations using TROPOMI data and machine learning over East Asia. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 288:117711. [PMID: 34329053 DOI: 10.1016/j.envpol.2021.117711] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 06/20/2021] [Accepted: 07/02/2021] [Indexed: 06/13/2023]
Abstract
In East Asia, air quality has been recognized as an important public health problem. In particular, the surface concentrations of air pollutants are closely related to human life. This study aims to develop models for estimating high spatial resolution surface concentrations of NO2 and O3 from TROPOspheric Monitoring Instrument (TROPOMI) data in East Asia. The machine learning was adopted by fusion of various satellite-based variables, numerical model-based meteorological variables, and land-use variables. Four machine learning approaches-Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boost (XGB), and Light Gradient Boosting Machine (LGBM)-were evaluated and compared with Multiple Linear Regression (MLR) as a base statistical method. This study also modeled the NO2 and O3 concentrations over the ocean surface (i.e., land model for scheme 1 and ocean model for scheme 2). The estimated surface concentrations were validated through three cross-validation approaches (i.e., random, temporal, and spatial). The results showed that the NO2 model produced R2 of 0.63-0.70 and normalized root-mean-square-error (nRMSE) of 38.3-42.2% and the O3 model resulted in R2 of 0.65-0.78 and nRMSE of 19.6-24.7% for scheme 1. The indirect validation based on the stations near the coastline for scheme 2 showed slight decrease (~0.3-2.4%) in nRMSE when compared to scheme 1. The contributions of input variables to the models were analyzed based on SHapely Additive exPlanations (SHAP) values. The NO2 vertical column density among the TROPOMI-derived variables showed the largest contribution in both the NO2 and O3 models.
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Affiliation(s)
- Yoojin Kang
- Department of Urban & Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - Hyunyoung Choi
- Department of Urban & Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - Jungho Im
- Department of Urban & Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea.
| | - Seohui Park
- Department of Urban & Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - Minso Shin
- Department of Urban & Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - Chang-Keun Song
- Department of Urban & Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - Sangmin Kim
- Environmental Satellite Centre, Climate and Air Quality Research Department, Incheon, South Korea
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Feng X, Shen J, Yang H, Wang K, Wang Q, Zhou Z. Time-Frequency Analysis of Particulate Matter (PM 10) Concentration in Dry Bulk Ports Using the Hilbert-Huang Transform. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17165754. [PMID: 32784870 PMCID: PMC7460512 DOI: 10.3390/ijerph17165754] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 07/27/2020] [Accepted: 08/05/2020] [Indexed: 01/29/2023]
Abstract
To analyze the time–frequency characteristics of the particulate matter (PM10) concentration, data series measured at dry bulk ports were used to determine the contribution of various factors during different periods to the PM10 concentration level so as to support the formulation of air quality improvement plans around port areas. In this study, the Hilbert–Huang transform (HHT) method was used to analyze the time–frequency characteristics of the PM10 concentration data series measured at three different sites at the Xinglong Port of Zhenjiang, China, over three months. The HHT method consists of two main stages, namely, empirical mode decomposition (EMD) and Hilbert spectrum analysis (HSA), where the EMD technique is used to pre-process the HSA in order to determine the intrinsic mode function (IMF) components of the raw data series. The results show that the periods of the IMF components exhibit significant differences, and the short-period IMF component provides a modest contribution to all IMF components. Using HSA technology for these IMF components, we discovered that the variations in the amplitude of the PM10 concentration over time and frequency are discrete, and the range of this variation is mainly concentrated in the low-frequency band. We inferred that long-term influencing factors determine the PM10 concentration level in the port, and short-term influencing factors determine the difference in concentration data at different sites. Therefore, when formulating PM10 emission mitigation strategies, targeted measures must be implemented according to the period of the different influencing factors. The results of this study can help guide recommendations for port authorities when formulating the optimal layout of measurement devices.
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Affiliation(s)
- Xuejun Feng
- College of Habour, Coastal and Offshore Engineering, Hohai University, No.1, Xikang Road, Nanjing 210098, China; (X.F.); (K.W.)
| | - Jinxing Shen
- College of Civil and Transportation Engineering, Hohai University, No.1, Xikang Road, Nanjing 210098, China
- Correspondence:
| | - Haoming Yang
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Nanjing University of Information Science and Technology, No.219, Ningliu Road, Nanjing 210044, China;
| | - Kang Wang
- College of Habour, Coastal and Offshore Engineering, Hohai University, No.1, Xikang Road, Nanjing 210098, China; (X.F.); (K.W.)
| | - Qiming Wang
- College of Science, Hohai University, No.1, Xikang Road, Nanjing 210098, China; (Q.W.); (Z.Z.)
| | - Zhongguo Zhou
- College of Science, Hohai University, No.1, Xikang Road, Nanjing 210098, China; (Q.W.); (Z.Z.)
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Primary Air Pollutants Emissions Variation Characteristics and Future Control Strategies for Transportation Sector in Beijing, China. SUSTAINABILITY 2020. [DOI: 10.3390/su12104111] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Air pollutant emissions from vehicles, railways, and aircraft for freight and passenger transportation are major sources of air pollution, and strongly impact the air quality of Beijing, China. To better understand the variation characteristics of these emissions, we used the emission factor method to quantitatively determine the air pollutant emissions from the transportation sector. The emission intensity of different modes of transportation was estimated, and measures are proposed to prevent and control air pollutants emitted from the transportation sector. The results showed that air pollutant emissions from the transportation sector have been decreasing year by year as a result of the reduction in emissions from motor vehicles, benefiting from the structural adjustment of motor vehicles. A comparison of the emission intensity of primary air pollutants from different modes of transportation showed that the emission level of railway transportation was much lower than that of road transportation. However, Beijing relies heavily on road transportation, with road freight transportation accounting for 96% of freight transportation, whereas the proportion of railway transportation was low. Primary air pollutants from the transportation sector contributed significantly to the total emissions in Beijing. The proportion of NOX emissions increased from 54% in 2013 to 58% in 2018. To reduce air pollutant emissions from the transportation sector, further adjustments and optimization of the structure of transportation in Beijing are needed. As for the control of motor vehicle pollutant emissions, vehicle composition must be adjusted and the development of clean energy must be promoted, as well as the replacement of diesel vehicles with electric vehicles for passenger and freight transportation.
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