Spatiotemporal Variations and Uncertainty in Crop Residue Burning Emissions over North China Plain: Implication for Atmospheric CO2 Simulation

Large uncertainty exists in the estimations of greenhouse gases and aerosol emissions from crop residue burning, which could be a key source of uncertainty in quantifying the impact of agricultural fire on regional air quality. In this study, we investigated the crop residue burning emissions and their uncertainty in North China Plain (NCP) using three widely used methods, including statistical-based, burned area-based, and fire radiative power-based methods. The impacts of biomass burning emissions on atmospheric carbon dioxide (CO2) were also examined by using a global chemical transport model (GEOS-Chem) simulation. The crop residue burning emissions were found to be high in June and followed by October, which is the harvest times for the main crops in NCP. The estimates of CO2 emission from crop residue burning exhibits large interannual variation from 2003 to 2019, with rapid growth from 2003 to 2012 and a remarkable decrease from 2013 to 2019, indicating the effects of air quality control plans in recent years. Through Monte Carlo simulation, the uncertainty of each estimation was quantified, ranging from 20% to 70% for CO2 emissions at the regional level. Concerning spatial uncertainty, it was found that the crop residue burning emissions were highly uncertain in small agricultural fire areas with the maximum changes of up to 140%. While in the areas with large agricultural fire, i.e., southern parts of NCP, the coefficient of variation mostly ranged from 30% to 100% at the gridded level. The changes in biomass burning emissions may lead to a change of surface CO2 concentration during the harvest times in NCP by more than 1.0 ppmv. The results of this study highlighted the significance of quantifying the uncertainty of biomass burning emissions in a modeling study, as the variations of crop residue burning emissions could affect the emission-driven increases in CO2 and air pollutants during summertime pollution events by a substantial fraction in this region.

Evaluation of Failure Probability of Ships on the Baltic Sea by Means of Simulation Model and Statistical Data

The ship technical failures contribution in overall number of navigational accidents are significantly smaller than those caused by human factor but in safety analysis they cannot be neglected. The paper presents stochastic modeling the technical failures of ships with respect of most important ship systems such as main engine, power generators and steering gear. The data for simulation was obtained from analysis of ships statistical data of polish owners. Obtained results could be useful for estimation of transport loses or potential environmental threats and planning the response action against them.

Modeling energy use and emissions from North American shipping: application of the ship traffic, energy, and environment model

The waterway network ship traffic, energy, and environment model (STEEM) is applied to geographically characterize energy use and emissions for interport ship movement for North America, including the United States, Canada, and Mexico. STEEM advances existing approaches by (i) estimating emissions for large regions on the basis of nearly complete data describing historical ship movements, attributes, and operating profiles of individual ships, (ii) solving distances on an empirical waterway network for each pair of ports considering ship draft and width constraints, and (iii) allocating emissions on the basis of the most probable routes. We estimate that the 172 000 ship voyages to and from North American ports in 2002 consumed about 47 million metric tonnes of heavy fuel oil and emitted about 2.4 million metric tonnes of SO2. Comparison with port and regional studies shows good agreement in total estimates and better spatial precision than current top-down methods. In quantifying limitations of top-down approaches that assume existing proxies for ship traffic density are spatially representative across larger domains, we find that International Comprehensive Ocean-Atmosphere Data Set (ICOADS) proxy data are spatially biased, especially at small scales. Emissions estimated by STEEM for ships within 200 nautical miles of the coastal areas of the United States are about 5 times the emissions estimated in previous studies using cargo as a proxy.