Anthropogenic Source Contributions to Ozone Formation in the Greater Houston Area

The Houston-Galveston-Brazoria (HGB) area of Texas has historically experienced severe air pollution events with high concentrations of ozone (O3) during the summer season. This study evaluates the contribution of different anthropogenic sources to ozone formation in the HGB area. The Emission Processing System (EPS3) is used to process emission files in four different scenarios (Base case as including All emission sources (BC), All sources— Area sources (AMA), All sources—Point sources (AMP), and All sources— Mobile sources (AMM). These files are used as input in photochemical modeling with the Comprehensive Air Quality Model with Extensions (CAMx) to simulate ozone formation. The data is analyzed for daily maximum ozone concentrations and contribution of source categories at three air quality monitoring locations (La Porte Sylvan beach-C556, Houston Texas avenue-C411, and Texas city in Galveston-C683) for a study period of June 1-June 30, 2012. The contribution of the point sources to ozone formation is dominated at all three locations, followed by mobile sources and area sources on high ozone days. The relative contributions of point sources are 27.51% ± 3.53%, 21.45% ± 7.36%, and 30.30% ± 9.36%;and mobile sources are 18.27% ± 2.22%, 20.60% ± 6.89%, and 18.61% ± 7.43%;and area sources were 4.2% ± 1.65%, 5.21% ± 1.59%, and 3.72% ± 1.52% at C556, C411, and C683, respectively. These results demonstrate the importance of regulatory focus on controlling point and mobile source emissions for NAAQS attainment in the study region.

Determination of Air Pollutant Concentrations in Plant Species in Relation to Pollution Sources

Air quality has been a major health issue in urban areas in recent decades. Human activities release a large number of pollutants into the atmosphere which has a direct impact on plant health and leads to ecosystem degradation. The objective of this study is to contribute to a better evaluation of the impact of the air quality of the city of Togo on biological resources. The determination of pollutants was done on samples of plant species with a strong link with the source of pollution. The determination of Sulfur dioxide (SO2) was done by the ripper method. The determination of carbon and estimation of CO2 and CO by the colorimetric method. The determination of nitrogen was done by the Kjeldhal method. The results showed that at the industrial level the amount of CO2 in Alternanthera repens is high with a value of 53.3911 mg/ml. On the other hand, the quantity of CO in Senna occidentalis is 44.3619 mg/ml. In Pithecellobium dulce, the quantity of SO2 and NO2 are evaluated respectively to 0.1588 mg/ml and 0.3696 mg/ml. Regarding to the dumps, the quantity of CO2 in Newbouldia laevis is very high with a value of 65.8508 mg/ml. On the other hand the amount of CO in Senna occidentalis is 51.6106 mg/ml. The quantity of SO2 in Newbouldia laevis is 0.2101 mg/ml and NO2 in Ocimum canum is 0.2744 mg/ml. At the level of roads, the quantities of CO2 and CO in Eragrostis tenella are very high with values respectively equal to 74.4092 mg/ml and 62.2654 mg/ml. On the other hand, the amount of NO2 in Amaranthus sp is 0.2304 mg/ml and that of SO2 in Eragrostis Tenella is 0.1691 mg/ml. The use of a plant bioindicator sensitive to pollutants, allowed concluding that the air of the city of Lome is polluted. The concentration of carbon dioxide and carbon monoxide is much more evident in return when the health of plant species is threatened.

Variations of aminiums in fine particles at a suburban site in Guangzhou, China: Importance of anthropogenic and natural emissions

Amines are important nitrogen-containing compounds in fine particles(PM2.5)in the atmosphere.Observations are necessary for in-depth understanding on the characteristics,sources and atmospheric processes of aminiums.In this study,the observation of ten C_(1)–C_(4) aminiums in PM_(2.5) was conducted in January and March of 2021 in suburban Guangzhou.The concentration and composition of aminiums showed significant differences between the pollution episodes and non-episode periods.Seasonal difference was also observed between winter and spring.The influence of meteorological factors(i.e.,wind speed,atmospheric pressure,temperature and relative humidity)was investigated.The variations of aminiums were also affected by different sources.Anthropogenic sources were suggested to be major contributor to aminiums in the pollution episodes,while biological sources were important sources to aminiums in the non-episode periods,especially in spring.Positive matrix factorization receptor model was applied to investigate the source contributions,and four major sources were identified.The results show that vehicular emission,industrial production,biological emission and soil/dust were the major sources of aminiums.This study emphasizes the importance of source contribution and meteorological conditions on the variations of aminiums,which provides further understanding of organic nitrogen in the atmosphere.

Quantification of Central and Eastern China's atmospheric CH_(4) enhancement changes and its contributions based on machine learning approach

Methane is the second largest anthropogenic greenhouse gas,and changes in atmospheric methane concentrations can reflect the dynamic balance between its emissions and sinks.Therefore,the monitoring of CH_(4) concentration changes and the assessment of underlying driving factors can provide scientific basis for the government’s policy making and evaluation.China is the world’s largest emitter of anthropogenic methane.However,due to the lack of ground-based observation sites,little work has been done on the spatial-temporal variations for the past decades and influencing factors in China,especially for areas with high anthropogenic emissions as Central and Eastern China.Here to quantify atmospheric CH_(4) enhancements trends and its driving factors in Central and Eastern China,we combined the most up-to-date TROPOMI satellite-based column CH_(4)(xCH_(4))concentration from 2018 to 2022,anthropogenic and natural emissions,and a random forest-based machine learning approach,to simulate atmospheric xCH_(4) enhancements from 2001 to 2018.The results showed that(1)the random forest model was able to accurately establish the relationship between emission sources and xCH_(4) enhancement with a correlation coefficient(R^(2))of 0.89 and a root mean-square error(RMSE)of 11.98 ppb;(2)The xCH_(4) enhancement only increased from 48.21±2.02 ppb to 49.79±1.87 ppb from the year of 2001 to 2018,with a relative change of 3.27%±0.13%;(3)The simulation results showed that the energy activities and waste treatment were the main contributors to the increase in xCH_(4) enhancement,contributing 68.00% and 31.21%,respectively,and the decrease of animal ruminants contributed-6.70% of its enhancement trend.

Optimal reduction of anthropogenic emissions for air pollution control and the retrieval of emission source from observed pollutantsⅢ:Emission source inversion using a double correction iterative method

Using the incomplete adjoint operator method in part I of this series of papers,the total emission source S can be retrieved from the pollutant concentrationsρob obtained from the air pollution monitoring network.This paper studies the problem of retrieving anthropogenic emission sources from S.Assuming that the natural source Sn is known,and as the internal source Sc due to chemical reactions is a function of pollutant concentrations,if the chemical reaction equations are complete and the parameters are accurate,Sc can be calculated directly fromρob,and then Sa can be obtained from S.However,if the chemical reaction parameters(denoted asγ)are insufficiently accurate,bothγand Sc should be corrected.This article proposes a"double correction iterative method"to retrieve Sc and correctγand proves that this iterative method converges.