Trends in Emission Inventory of Marine Traffic for Port of Haifa

Located in the Middle East, Haifa Port serves both local and international trade interests (from Asia, Europe, America, Africa, etc.). Due to its strategic location, the port is part of the Belt and Road initiative. This research investigates Haifa Port’s emissions contribution to the existing daily emission inventory level in the area. This research is based on a developed full bottom-up model framework that looks at the single vessel daily voyage through its port call stages. The main data sources for vessel movements used in this research are the Israel Navy’s movements log and the Israel Administration of Shipping and Ports’ (ASP) operational vessel movements and cargo log. The Fuel Consumption (FC) data and Sulfur Content (SC) levels are based on official Israel ASP survey data. The observation years in this research are 2010–2018, with a focus on the Ocean-Going Vessel (OGV) type only. The results show that the vessel fleet calling at Israel ports mainly comprises vessels that have a lower engine tier grade (i.e., Tier 0 and 1), which is considered a heavy contributor to nitrogen oxide (NOx) pollution. The study recommends an additional cost charged (selective tariff) to reflect the external social cost linked to the single vessel air pollution combined with supportive technological infrastructure and economic incentive tools (e.g., electric subsidy) to attract or influence vessel owners to assign vessels equipped with new engine tier grades for calls at Israeli ports.

Updated national emission inventory and comparison with the Emissions Database for Global Atmospheric Research (EDGAR): case of Lebanon

Physically based computational modeling is an effective tool for estimating and predicting the spatial distribution of pollutant concentrations in complex environments. A detailed and up-to-date emission inventory is one of the most important components of atmospheric modeling and a prerequisite for achieving high model performance. Lebanon lacks an accurate inventory of anthropogenic emission fluxes. In the absence of a clear emission standard and standardized activity datasets in Lebanon, this work serves to fill this gap by presenting the first national effort to develop a national emission inventory by exhaustively quantifying detailed multisector, multi-species pollutant emissions in Lebanon for atmospheric pollutants that are internationally monitored and regulated as relevant to air quality. Following the classification of the Emissions Database for Global Atmospheric Research (EDGAR), we present the methodology followed for each subsector based on its characteristics and types of fuels consumed. The estimated emissions encompass gaseous species (CO, NOx, SO2), and particulate matter (PM2.5 and PM10). We compare totals per sector obtained from the newly developed national inventory with the international EDGAR inventory and previously published emission inventories for the country for base year 2010 presenting current discrepancies and analyzing their causes. The observed discrepancies highlight the fact that emission inventories, especially for data-scarce settings, are highly sensitive to the activity data and their underlying assumptions, and to the methodology used to estimate the emissions.

Optimization and Evaluation of SO2 Emissions Based on WRF-Chem and 3DVAR Data Assimilation

Emission inventories are important for modeling studies and policy-making, but the traditional “bottom-up” emission inventories are often outdated with a time lag, mainly due to the lack of accurate and timely statistics. In this study, we developed a “top-down” approach to optimize the emission inventory of sulfur dioxide (SO2) using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and a three-dimensional variational (3DVAR) system. The observed hourly surface SO2 concentrations from the China National Environmental Monitoring Center were assimilated and used to estimate the gridded concentration forecast errors of WRF-Chem. The concentration forecast errors were then converted to the emission errors by assuming a linear response from SO2 emission to concentration by grids. To eliminate the effects of modelling errors from aspects other than emissions, a strict data-screening process was conducted. Using the Multi-Resolution Emission Inventory for China (MEIC) 2010 as the a priori emission, the emission inventory for October 2015 over Mainland China was optimized. Two forecast experiments were conducted to evaluate the performance of the SO2 forecast by using the a priori (control experiment) and optimized emissions (optimized emission experiment). The results showed that the forecasts with optimized emissions typically outperformed the forecasts with 2010 a priori emissions in terms of the accuracy of the spatial and temporal distributions. Compared with the control experiment, the bias and root-mean-squared error (RMSE) of the optimized emission experiment decreased by 71.2% and 25.9%, and the correlation coefficients increased by 50.0%. The improvements in Southern China were more significant than those in Northern China. For the Sichuan Basin, Yangtze River Delta, and Pearl River Delta, the bias and RMSEs decreased by 76.4–94.2% and 29.0–45.7%, respectively, and the correlation coefficients increased by 23.5–53.4%. This SO2 emission optimization methodology is computationally cost-effective.

EVOLUTION OF POLLUTANTS EMISSIONIN RELATION TOROAD TRAFFIC IN THE CITY OF LOME (TOGO) FROM 2010 TO 2019

The automobile fleet in Togo has increased in the last decades with a patchwork of vehicles that are in majority older than ten (10) years. Until 2019, the car fleet in Togo was almost dependent upon petroleum products, and was consequentlya source of air pollutants emission. Lome is the capital city of Togo with the characteristic of having the highest road traffic volume that significantly impacts air quality. In accordance with the EMEP/EEA air pollutant emission inventory guide and the COPERT method, emissions of carbone monoxide (CO), nitrogen oxides (NOx), non-methane volatile organic compounds (NMVOCs) and particulate matter (PM) are respectively estimated to: 2621.674 tCO 82.444 tNOx 558.778 tNMVOC and 7.241 tPM. In the time series 2010-2019, emissions of CO, NMVOCs and NOx fell overall with average yearly rates by respectively 83,0234 66,4888 and 0,8073 t/year whereas the PM emission rose(0,8208 t/year).

Analysis of Carbon Emission Energy Inventory from Refrigerant Production and Recycling Carbon Compensation

At present, the massive emissions of carbon dioxide and nitrogen oxides and other greenhouse gases caused by human activities have caused more and more serious negative effects on global climate change. In order to cope with global warming and achieve sustainable development, achieve “carbon neutrality” as soon as possible. In the refrigeration industry, it is necessary to reduce greenhouse gas emissions related to refrigerants, including the production, use, and recycling of refrigerants. This paper has carried out the calculation of greenhouse gas emissions during the refrigerant preparation process, and compared and analyzed the emission reductions of refrigerant recycling and reuse; the research based on the energy consumption of the refrigerant production process uses the greenhouse gas emission inventory analysis method to Taking refrigerant R134a as an example, the carbon emission accounting boundary of the production process is set, the emission source is determined, the emission is calculated based on the emission factor method, and the emission inventory is established; the carbon offset effect of the recycling and reuse of the refrigerant is analyzed. The research results show that if the entire refrigerant industry fully recycles waste refrigerants, it can reduce carbon emissions by about 29.7% compared to just producing new refrigerants.

Spatial Analysis of the Ship Gas Emission Inventory in the Port of Busan Using Bottom-Up Approach Based on AIS Data

Dense hub port-cities have been suffering from ship gas emissions causing atmospheric pollution and a threat to the health of coastal residents. To control ship gas emissions, many regulations have been established internationally. Analyses of ship gas emission inventories are essential to quantify mass and track emission changes over time in a given geographical area. Based on the gas emissions inventory, applicable regulations such as Emission Control Area (ECA) realization and Vessel Speed Reduction (VSR) may be established. The ship gas emission inventory (CO2, CO, NOx, SOx and PM) from the Busan Port (BP), including the North Port (NP) and Gamcheon Dadae-po Port (GDP), which is the biggest port in the Republic of Korea and which is also surrounded by residential, commercial, and industrial areas, were spatially analyzed. To calculate geographical ship gas emissions in real-time, this study introduces a bottom-up methodology using Automatic Identification System (AIS) data. According to the geographical density analysis of the gas emissions inventory, this study highlights that about 35% of the annual ship gas emissions of BP in 2019 were concentrated in the passageway to NP because of high ship speeds when leaving or arriving at the port. To protect the health of coastal residents, ship speed limit regulations along the passageway should be revised based on our spatial analysis results. The spatial analysis of the ship gas emission inventory in BP will be useful basic data for properly evaluating the local gas emission state on newly established or revised environmental regulations for BP.

AN APPLICATION OF MACHINE LEARNING TO SHIPPING EMISSION INVENTORY

The objective of this study is to develop a shipping emission inventory model incorporating Machine Learning (ML) tools to estimate gaseous emissions. The tools enhance the emission inventories which currently rely on emission factors. The current inventories apply varied methodologies to estimate emissions with mixed accuracy. Comprehensive Bottom-up approach have the potential to provide very accurate results but require quality input. ML models have proven to be an accurate method of predicting responses for a set of data, with emission inventories an area unexplored with ML algorithms. Five ML models were applied to the emission data with the best-fit model judged based on comparing the real mean square errors and the R-values of each model. The primary gases studied are from a vessel measurement campaign in three modes of operation; berthing, manoeuvring, and cruising. The manoeuvring phase was identified as key for model selection for which two models performed best.