Observations of ozone and carbon monoxide at Mei-Feng mountain site (2269 m a.s.l.) in Central Taiwan: seasonal variations and influence of Asian continental outflow

Revisiting regional and seasonal variations in decadal carbon monoxide variability: Global reversal of growth rate

Carbon monoxide (CO) is one of the important trace gases in the atmosphere capturing the evolution of chemical properties of the troposphere. Here we analyze the growth rates of CO during the period of 1991-2020 using in situ measurements from the World Meteorological Organization's (WMO) Global Atmospheric Watch (GAW) program. The analysis of trends has been done on different spatial and temporal scales. Our analysis supports the decline in the overall CO mixing ratios over the globe but inter-decadal and regional trend analysis has shown heterogeneous changes in the given period of study. On average, there has been a decrease of -16.22 ± 1.92 ppb and -4.5 ± 0.64 ppb observed at the sites in the northern hemisphere (NH) and southern hemisphere (SH), respectively. This decline occurred at rates of -0.80 ± 0.12 ppb yr-1 in the NH and - 0.12 ± 0.03 ppb yr-1 in the SH. Bifurcating the annual trends for seasonal analysis reveals the impact of emissions, chemistry and atmospheric transport on CO variation over different regional clusters of stations. Seasonal trend analysis provides further evidence regarding heterogeneous patterns in the South-East Asia region. Our study highlights a slowdown in CO decline during the 2011-2020 decade when compared to the rate of decrease observed in 2001-2010. This is inferred from the variability and much slower decline of CO emissions across different regions, contributing to a weakening in CO trends.

Fog and rain water chemistry in a tea plantation of northern Taiwan

Tea plantations are expanding globally and many are in mountainous areas with frequent fog but few studies have examined fog chemistry in these areas. We examined chemical composition of fog and rain water at a tea plantation in northern Taiwan. Fog water was collected using a Kroneis passive cylindrical fog-water collector and rain water was collected using a 20-cm-diameter funnel. The most abundant ions were Cl- and Na+ in both fog and rain waters due to the proximity of the site to the coast. The order of abundance of other ions was NO3-  > Mg2+  > SO42-  > Ca2+  > NH4+  > K+  > H+ in fog water and SO42-  > K+  > NO3-  > NH4+  > Ca2+  > Mg2+  > H+ in rain water. The concentration enrichment ratio (fog to rain) ranged between 2.2 (K+) and 22 (Mg2+) lying between sites near major emission sources and sites in remote areas, possibly because the immediate surrounding landscape is covered with secondary forests although it is near large cities. Factor analysis highlights the influences of sea-salt aerosols on the variation of fog and rain water chemistry. Sea-salt corrections using Na+ as the sea salt tracer led to negative concentrations of Cl- and Mg2+ suggesting that assumptions involved in sea-salt corrections were not satisfied. Agriculture influence is identified as a unique factor for explaining variance of K+, NH4+, and dissolved organic nitrogen (DON) concentrations in fog water but not rain water. Ion concentrations in fog and rain water were generally higher in the weekly samples associated with air trajectories passing through the continental East Asia than those associated with oceanic trajectories pointing to the role of regional pollution sources in affecting local fog and rain water chemistry. Our study highlights greater effects of tea agriculture on fog than rain water chemistry.

Decrease of atmospheric black carbon and CO2 concentrations due to COVID-19 lockdown at the Mt. Waliguan WMO/GAW baseline station in China

The Coronavirus Disease 2019 (COVID-19) lockdown policy reduced anthropogenic emissions and impacted the atmospheric chemical characteristics in Chinese urban cities. However, rare studies were conducted at the high mountain site. In this work, in-situ measurements of light absorption by carbonaceous aerosols and carbon dioxide (CO₂) concentrations were conducted at Waliguan (WLG) over the northeastern Tibetan Plateau of China from January 3 to March 30, 2020. The data was employed to explore the influence of the COVID-19 lockdown on atmospheric chemistry in the background-free troposphere. During the sampling period, the light absorption near-infrared (>470 nm) was mainly contributed by BC (>72%), however, BC and brown carbon (BrC) contributed equally to light absorption in the short wavelength (∼350 nm). The average BC concentrations in the pre-, during and post-lockdown were 0.28 ± 0.25, 0.18 ± 0.16, and 0.28 ± 0.20 μg m^-3, respectively, which decreased by approximately 35% during the lockdown period. Meanwhile, CO₂ also showed slight decreases during the lockdown period. The declined BC was profoundly attributed to the reduced emissions (∼86%), especially for the combustion of fossil fuels. Moreover, the declined light absorption of BC, primary and secondary BrC decreased the solar energy absorbance by 35, 15, and 14%, respectively. The concentration weighted trajectories (CWT) analysis suggested that the decreased BC and CO₂ at WLG were exclusively associated with the emission reduction in the eastern region of WLG. Our results highlighted that the reduced anthropogenic emissions attributed to the lockdown in the urban cities did impact the atmospheric chemistry in the free troposphere of the Tibetan Plateau.

Study on CO data filtering approaches based on observations at two background stations in China

The identification of regional representative carbon monoxide (CO) measurements that are minimally influenced by local sources/sinks is essential to understand the characteristics of atmospheric CO over a certain region. In this study, three commonly used data filtering approaches were applied to atmospheric CO data obtained from 2010/2011 to 2017 at two World Meteorological Administration/Global Atmospheric Programme (WMO/GAW) regional stations (Lin'an, LAN and Shangdianzi, SDZ) in China, to study their applicability for individual stations. The three methods used were the meteorological conditions (MET), statistical approaches (robust extraction of baseline signal, REBS), and the time scale of the CO variations (standard deviations of the running mean, SDM). The results from the three methods displayed almost the same seasonal cycles at LAN but different variations at SDZ. They each extracted similar yearly CO growth rates at LAN, but there was a large difference at SDZ, with values of -10.6 ± 0.5, -2.2 ± 0.1, and - 23.5 ± 0.3 ppb yr^-1 for MET, REBS, and SDM, respectively. The slight decrease observed using REBS at SDZ was mainly due to the biased distribution of CO records, which was a purely statistical method that did not consider topography or meteorological conditions. Thus, the REBS method should be applied cautiously to CO observations at stations like SDZ. The SDM method may overestimate multi-year trends. Among the three approaches, MET may be the most suitable for filtering CO observation records, especially at stations like SDZ with special geographical and meteorological conditions in economically-developed regions.

Seasonal trends, meteorological impacts, and associated health risks with atmospheric concentrations of gaseous pollutants at an Indian coastal city

This study presents surface ozone (O3) and carbon monoxide (CO) measurements conducted at Bhubaneswar from December 2010 to November 2012 and attempts for the very first time a health risk assessment of the atmospheric trace gases. Seasonal variation in average 24 h O3 and CO shows a distinct winter (December to February) maxima of 38.98 ± 9.32 and 604.51 ± 145.91 ppbv, respectively. O3 and CO characteristics and their distribution were studied in the form of seasonal/diurnal variations, air flow patterns, inversion conditions, and meteorological parameters. The observed winter high is likely due to higher regional emissions, the presence of a shallower boundary layer, and long-range transport of pollutants from the Indo-Gangetic Plain (IGP). Large differences between daytime and nighttime O3 values during winter compared to other seasons suggest that photochemistry is much more active on this site during winter. O3 and CO observations are classified in continental and marine air masses, and continental influence is estimated to increase O3 and CO by up to 20 and 120 ppbv, respectively. Correlation studies between O3 and CO in various seasons indicated the role of CO as one of the O3 precursors. Health risk estimates predict 48 cases of total premature mortality in adults due to ambient tropospheric O3 during the study period. Comparatively low CO concentrations at the site do not lead to any health effects even during winter. This study highlights the possible health risks associated with O3 and CO pollution in Bhubaneswar, but these results are derived from point measurements and should be complemented either with regional scale observations or chemical transport models for use in design of mitigation policies.

The role of precursor gases and meteorology on temporal evolution of O₃ at a tropical location in northeast India

South Asia, particularly the Indo-Gangetic Plains and foothills of the Himalayas, has been found to be a major source of pollutant gases and particles affecting the regional as well as the global climate. Inventories of greenhouse gases for the South Asian region, particularly the sub-Himalayan region, have been inadequate. Hence, measurements of the gases are important from effective characterization of the gases and their climate effects. The diurnal, seasonal, and annual variation of surface level O3 measured for the first time in northeast India at Dibrugarh (27.4° N, 94.9° E, 111 m amsl), a sub-Himalayan location in the Brahmaputra basin, from November 2009 to May 2013 is presented. The effect of the precursor gases NO x and CO measured simultaneously during January 2012-May 2013 and the prevailing meteorology on the growth and decay of O3 has been studied. The O3 concentration starts to increase gradually after sunrise attaining a peak level around 1500 hours LT and then decreases from evening till sunrise next day. The highest and lowest monthly maximum concentration of O3 is observed in March (42.9 ± 10.3 ppb) and July (17.3 ± 7.0 ppb), respectively. The peak in O3 concentration is preceded by the peaks in NO x and CO concentrations which maximize during the period November to March with peak values of 25.2 ± 21.0 ppb and 1.0 ± 0.4 ppm, respectively, in January. Significant nonlinear correlation is observed between O3 and NO, NO2, and CO. National Atmospheric and Oceanic Administration Hybrid Single-Particle Lagrangian Integrated Trajectory back-trajectory and concentration weighted trajectory analysis carried out to delineate the possible airmass trajectory and to identify the potential source region of NO x and O3 concentrations show that in post-monsoon and winter, majority of the trajectories are confined locally while in pre-monsoon and monsoon, these are originated at the Indo-Gangetic plains, Bangladesh, and Bay of Bengal.

Combining a road pollution dispersion model with GIS to determine carbon monoxide concentration in Tennessee

The purpose of this paper is to develop an air pollution model that is independent from pollution monitoring sites and highly accurate through space and time. Total carbon monoxide concentration is computed with the use of traffic flow data, vehicle speed and dimensions, emission rates, wind speed, and temperature. The data are interpolated using a geographic information system universal kriging technique, and the end results produce state level air pollution maps with high local accuracy. The model is validated against Environment Protection Agency (EPA) pollution data. Overall, the model has 71 % agreement with EPA, overestimating values of carbon monoxide for less than 1 ppm. The model has three advantages over already assessed air pollution models. First, it is completely independent of any air pollution monitoring stations; thus, possible temporary or permanent unreliability or lack of the data is avoided. Second, being based on a 5,710 traffic count network, the problem of remote places coverage is avoided. Third, it is based on a straightforward equation, where minimal preprocessing of traffic and climatic data is required.