Adoption of cleaner technologies and reduction in fire events in the hotspots lead to global decline in carbon monoxide

Carbon Monoxide (CO) is not a greenhouse gas (GHG), but has the capacity to change atmospheric chemistry of other GHGs such as methane and ozone, and therefore indirectly affects Earth's radiative forcing of the GHGs and surface temperature. Here, we use the CO mixing ratio at 850 hPa from the Tropospheric Emission Spectrometer (TES) reanalysis and the Measurement of Pollution in the Troposphere (MOPITT) satellite measurements for the period 2005-2019 to examine the spatio-temporal changes in CO across the latitudes. We find a substantial decrease in global CO, about 0.21 ± 0.09 ppb/yr (0.23 ± 0.12%/yr) with the TES data and about 0.36 ± 0.07 ppb/yr (0.45 ± 0.08%/yr) with the MOPITT satellite measurements during the study period. The highest CO decreasing trend is observed in Eastern China (2.7 ± 0.37 ppb/yr) followed by Myanmar (2.142 ± 0.59 ppb/yr) and South America (1.08 ± 0.82 ppb/yr). This negative trend in CO is primarily due to the decrease in biomass burning and stringent environmental regulations in the respective regions and countries. The sources including road transport that account for about 33.6% of CO emissions, followed by industries (18.3%) and agricultural waste burning (8.8%), might also be responsible for the reduction in CO due to adaptation of improved emission control technology and regulations in the past decade from 2005 to 2019. Therefore, the study provides new insights on the current trends of global CO distribution and reasons for recent reduction in global CO emissions, which would be useful for future decision-making process to control air pollution.

Source apportionment of carbon monoxide over India: a quantitative analysis using MOZART-4

MOZART-4 chemistry transport model has been used to examine the contribution of carbon monoxide (CO) from different source regions/types by tagging their emissions in model simulations. These simulations are made using tagged tracer approach to estimate the relative contribution of different geographical regions and different emission sources, such as anthropogenic or biomass burning to the CO concentration at the surface, in the planetary boundary layer (PBL), and in the free troposphere (FT) over the Indian sub-continent. The CO budget analyses highlight the significant contribution of the Indian emissions on surface CO and influence of chemical production on the free tropospheric CO concentration. The total CO mixing ratio is estimated as 263 ± 139 parts per billion by volume (ppbv) for surface, 177 ± 71 ppbv for PBL, and 112 ± 14 ppbv for FT. The percentage contributions of primary sources are found to be 80%, 68%, and 53% at the surface, in the PBL, and in the FT, respectively. The sub-regional analysis of India shows that anthropogenic and photochemical processes contribute 41-75% and 15-46% CO, respectively, at the surface. Maximum percentage contribution of anthropogenic CO is observed over Indo-Gangetic Plain and Eastern India (75%). CO contribution from local anthropogenic and biomass burning emissions and transported from other global source regions are analyzed over the Indian region at the surface, in the PBL, and in the FT. The local anthropogenic sources contribute largest to the surface CO over India with 108 ppbv, followed by China with 98 ppbv, Europe with 55 ppbv, North America (NA) with 46 ppbv, and South-east Asia (SEA) and Middle East (ME) with 23 ppbv each. India's PBL (FT) CO is mostly influenced by China's anthropogenic emissions with 12 ppbv (8 ppbv) followed by SEA with 7 ppbv (6 ppbv). Surface biomass burning CO over India (6 ppbv) is much lower than in other regions such as SEA (32 ppbv), Africa (24 ppbv), and South America (11 ppbv). In the PBL (FT), SEA and Africa's BB emissions show major impact on CO over India with 6 ppbv (5 ppbv) and 5 ppbv (4 ppbv), respectively.

Pollution Events at the High-Altitude Mountain Site Zugspitze-Schneefernerhaus (2670 m a.s.l.), Germany

Within the CO2 time series measured at the Environmental Research Station Schneefernerhaus (UFS), Germany, as part of the Global Atmospheric Watch (GAW) program, pollution episodes are traced back to local and regional emissions, identified by δ13C(CO2) as well as ratios of CO and CH4 to CO2 mixing ratios. Seven episodes of sudden enhancements in the tropospheric CO2 mixing ratio are identified in the measurements of mixing/isotopic ratios during five winter months from October 2012 to February 2013. The short-term CO2 variations are closely correlated with changes in CO and CH4 mixing ratios, achieving mean values of 6.0 ± 0.2 ppb/ppm for CO/CO2 and 6.0 ± 0.1 ppb/ppm for CH4/CO2. The estimated isotopic signature of CO2 sources (δs) ranges between −35‰ and −24‰, with higher values indicating contributions from coal combustion or wood burning, and lower values being the result of natural gas or gasoline. Moving Keeling plots with site-specific data selection criteria are applied to detect these pollution events. Furthermore, the HYSPLIT trajectory model is utilized to identify the trajectories during periods with CO2 peak events. Short trajectories are found covering Western and Central Europe, while clean air masses flow from the Atlantic Ocean and the Arctic Ocean.

Source Apportionment and Data Assimilation in Urban Air Quality Modelling for NO2: The Lyon Case Study

Developing effective strategies for reducing the atmospheric pollutant concentrations below regulatory threshold levels requires identifying the main origins/sources of air pollution. This can be achieved by implementing so called source apportionment methods in atmospheric dispersion models. This study presents the results of a source apportionment module implemented in the SIRANE urban air-quality model. This module uses the tagged species approach and includes two methods, named SA-NO and SA-NOX, in order to evaluate the sources’ contributions to the NO 2 concentrations in air. We also present results of a data assimilation method, named SALS, that uses the source apportionment estimates to improve the accuracy of the SIRANE model results. The source apportionment module and the assimilation method have been tested on a real case study (the urban agglomeration of Lyon, France, for the year 2008) focusing on the NO 2 emissions and concentrations. Results of the source apportionment with the SA-NO and SA-NOX models are similar. Both models show that traffic is the main cause of NO 2 air pollution in the studied area. Results of the SALS data assimilation method highlights its ability in improving the predictions of an urban atmospheric models.