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are important for modeling studies and policy-making, but the traditional “bottom-up” emission inventories are often outdated with a time lag, mainly due to the lack of accurate and timely statistics.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.

A Review on Impact of Anthropogenic Sulphur Dioxide Pollution on Health and Environment

The explosive global change in the emissions of Sulphur dioxide (SO2) over the decades have brought about tremendous effects in the regional as well as global scale in the composition and quality of air that we breathe. The global SO2 emission peaked in the 1970s, decreased until 2000 and has risen to peak again mainly as the result of increased globalization in developing countries. The global estimation is surprisingly low but the regional output of SO2 is high. The main hotspots being in East Asia Countries. The Sulphur aerosols not only impacts the animals’ health, but those of the ecosystems at large. SO2 contributes to global warming, ozone depletion, acid rain, smog etc. that has brought disastrous environmental conditions. WHO recommends a maximum exposure of not more than 0.5 ppm for around 24 hours’ span. SO2 reacts with other gases to form sulphate particles, constituent of particulate matters i.e. PM 2.5 concentrations, the exposure to which causes skin, respiratory, reproductive failures and cardiovascular ailments. This study, as such, is meant to bring into light the sources of SO2 and its harmful effects on health and environment at large. Air pollution is a major issue with adverse effects prevailing over the globe at present.

A sulfur dioxide Covariance-Based Retrieval Algorithm (COBRA): application to TROPOMI reveals new emission sources

Sensitive and accurate detection of sulfur dioxide (SO2) from space is important for monitoring and estimating global sulfur emissions. Inspired by detection methods applied in the thermal infrared, we present here a new scheme to retrieve SO2 columns from satellite observations of ultraviolet back-scattered radiances. The retrieval is based on a measurement error covariance matrix to fully represent the SO2-free radiance variability, so that the SO2 slant column density is the only retrieved parameter of the algorithm. We demonstrate this approach, named COBRA, on measurements from the TROPOspheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor (S-5P) satellite. We show that the method reduces significantly both the noise and biases present in the current TROPOMI operational DOAS SO2 retrievals. The performance of this technique is also benchmarked against that of the principal component algorithm (PCA) approach. We find that the quality of the data is similar and even slightly better with the proposed COBRA approach. The ability of the algorithm to retrieve SO2 accurately is further supported by comparison with ground-based observations. We illustrate the great sensitivity of the method with a high-resolution global SO2 map, considering 2.5 years of TROPOMI data. In addition to the known sources, we detect many new SO2 emission hotspots worldwide. For the largest sources, we use the COBRA data to estimate SO2 emission rates. Results are comparable to other recently published TROPOMI-based SO2 emissions estimates, but the associated uncertainties are significantly lower than with the operational data. Next, for a limited number of weak sources, we demonstrate the potential of our data for quantifying SO2 emissions with a detection limit of about 8 kt yr−1, a factor of 4 better than the emissions derived from the Ozone Monitoring Instrument (OMI). We anticipate that the systematic use of our TROPOMI COBRA SO2 column data set at a global scale will allow missing sources to be identified and quantified and help improve SO2 emission inventories.

Assessing the Responses of Aviation-Related SO2 and NO2 Emissions to COVID-19 Lockdown Regulations in South Africa

Aircraft emit harmful substances, such as carbon dioxide (CO2), water vapour (H2O), nitrogen oxides (NOx), sulphur oxides (SOx), particulates, and other trace compounds. These emissions degrade air quality and can deteriorate human health and negatively impact climate change. Airports are the nucleus of the ground and low-altitude emissions from aircraft during approach, landing, take-off, and taxi. During the global lockdown due to the COVID-19 pandemic, tight restrictions of the movement were imposed, leading to temporary closures of airports globally. In this study, we look at the variability of emissions at two major airports in South Africa, namely the OR Tambo international airport (FAOR) and the Cape Town international airport (FACT). Trend analysis of aircraft movements, i.e., departures and arrivals, showed a sharp decline at the two airports coinciding with the lockdowns to prevent the spread of the COVID-19. Consequently, a decrease in NO2 emissions by 70.45% (12.6 × 10−5 mol/m2) and 64.58% (11.6 × 10−5 mol/m2) at FAOR and FACT were observed, respectively. A noticeable SO2 emission decline was also observed, particularly over FAOR during the lockdown period in South Africa. Overall, this study observed that the global lockdown regulations had a positive impact on the air quality, causing a brief decline in emissions from commercial aviation at the South African major airports.

Data on Emission Factors of Gaseous Emissions from Combustion of Woody Biomasses as Potential Fuels for Firing Thermal Power Plants in Nigeria

This work generated data on the emission factors of air emissions from combustion of woody biomasses collected from southwest, Nigeria. This was with a view to finding their potentials as sustainable and environmentally friendly fuels for firing thermal power plants compared to coals. The data on heating values and elemental contents (carbon, sulphur and nitrogen) responsible for gaseous emissions in the 100 woody biomasses were collected from the previous results of this work to determine the gaseous emission factors on the expected condition of complete combustion. The current results showed that the CO2 emission factors ranged from 0.0147 kg/(MJ/kg) for Ficus mucuso to 0.1499 kg/(MJ/kg) for Spondias mombin, SO2 emission factors ranged from 0.0000000 kg/(MJ/kg) for Pterygota macrocarpa, Irvingia grandifolia, and fifteen others, to 0.0011341kg/(MJ/kg) for Khaya ivorensis, while NO2 emission factors ranged from 0.0000000 kg/(MJ/kg) for Citrus medica to 0.0035824 kg/(MJ/kg) for Ficus carica. Considering the minimal emissions from biomasses compared to coal species, serious political will is needed on the part of the Nigerian government to propagate these biomasses for fuels in firing the thermal plants in the country.

The Effect of Green R&D Activities on China’s SO2 Emissions: Evidence from a Panel Threshold Model

Previous studies on the effectiveness of improving sustainable development have acknowledged the importance of domestic research and development (R&D) activities. However, these studies remain general and ambiguous because they assume that all R&D activities are related to energy-saving and sustainable development. The corresponding empirical evidence is scabrous and ambiguous. In this paper, we focus on the effect of green innovation R&D activities on SO2 emission which is an important greenhouse gas affect global climate change and eco-civilization. Considering that there is heterogeneity exists in the innovation activities, the R&D activities are divided into three performers with two purposes. The empirical results based on a Chinese inter-provincial dataset of 2000-2016 suggest that the green innovation R&D activities are crucial for the reduction of the SO2 emission. However, the innovation R&D activities of different purposes and performers show statistically differentiated effects on SO2 emission. The major positive effect of green innovation R&D activities on SO2 emissions reduction is mainly from enterprises and utility-type of R&D activities. A further study based on the panel threshold also indicates that effects of green innovation R&D activities on SO2 emissions are nonlinear, depending on the technology absorptive ability.