Air pollutants from shipping: Costs of NOx emissions to the Baltic Sea

A comprehensive OBD data analysis framework: Identification and factor analysis of high-emission heavy-duty vehicles

On-Board Diagnostic (OBD) systems enable real-time monitoring of NOx emissions from heavy-duty diesel vehicles (HDDVs). However, few studies have focused on the root cause analysis of these emissions using OBD data. To address this gap, this study proposes an integrated analysis framework for HDDV NOx emissions that combines data processing, high-emission vehicle identification, and emission cause analysis. The framework employs a fuel-based window method to identify high-emission vehicles, while binning and machine learning techniques trace the causes of NOx emissions. A case study is conducted using data from 32 vehicles sourced from Tianjin On-Board Diagnostic Platform. Of these, five vehicles were identified as high emitters. A machine learning model was trained for each vehicle, with a detailed analysis conducted on three of them. The analysis involves a preliminary investigation of vehicle emissions status, followed by bin analysis to initially identify the causes of emissions. Finally, machine learning analysis is conducted, including the generation of individual conditional expectation (ICE) plots and multivariable partial dependence plots (PDPs), serving as a supplement to bin analysis when it cannot effectively pinpoint the causes of high emissions. This approach effectively uncovers the underlying factors within OBD big data. Using the analysis framework, we discover the identified causes of high NOx emissions were uneven heating of the Selective Catalytic Reduction (SCR) system and prolonged idling and high-power operation, catalyst degradation at 200-250 °C, and SCR system failure before 425 °C. The proposed framework offers a clear approach for identifying the causes of NOx emissions, aiding policymakers in implementing effective NOx control strategies for HDDVs.

Effects of shipping on non-indigenous species in the Baltic Sea

Shipping is regarded as an important vector for aquatic non-indigenous species (ANIS) worldwide. Less attention has been paid to its role in relation to environmental and economic causes of introduction and establishment, the knowledge of which is necessary to assess effects of changes in regulations on shipping. The purpose of this study was to estimate the impact of shipping on the incidence of ANIS in the Baltic Sea compared with environmental and economic factors. To this end, a production function was estimated with count data on ANIS (response variable) and shipping, environmental and economic factors as explanatory variables. Regression results from different regression models showed that shipping has a significant impact on ANIS incidence and can account for up to 38% of the number of ANIS in the sea. Predictions of the impact of measures implementing the Convention for the Control and Management of Ships' Ballast Water and Sediment indicated a reduction by 17% in the number of ANIS, which was counteracted by an expected increase in shipping traffic.

Valuating Natural Resources and Ecosystem Services: Systematic Review of Methods in Use

The relevance of an ecosystem approach, which involves addressing ecosystems as an object of research, economically evaluating ecosystem services, and including the existing variety of evaluation methods and their classifications for the estimation of nature’s value, was the focus of this study. So, the aim of the current research is to develop an evaluation theory by refining approaches and methods for the economic evaluation of natural resources and ecosystem services. The research object was the evaluation practice of the former USSR, Russia, and countries outside Russia. Employing research methods of systematization and content analysis with evolutionary and ecosystem approaches, about three hundred scientific papers have been the subject of this review. The study (1) reveals the evolutionary changes in economic evaluation approaches and methods of natural resources and ecosystem services; (2) discloses the features of the existing classifications of economic evaluation methods; and (3) offers the author’s classification, which is based on the five classification criteria: evaluation type, evaluation approaches, evaluation character (nature), evaluation methods, and market discourse. We believe that understanding the development of scientific thought about evaluation methods and their classifications will make it possible to increase the reliability of the estimation results in natural resource and environmental economics.