Estimation of atmospheric mixing heights using data from airport meteorological stations

Impact of meteorological conditions at multiple scales on ozone concentration in the Yangtze River Delta

Tropospheric ozone is known to have adverse effects on human health. Ozone pollution events are often associated with specific atmospheric circulation conditions. Therefore, studying the relationship between atmospheric circulation and ozone is particularly important for early warning and forecasting of ozone pollution events. Focusing on the Yangtze River Delta region, particularly in four important large industrial cities (Xuzhou, Nanjing, Shanghai, and Hangzhou) in the Yangtze River Delta, the T-mode objective classification method was applied to classify the weather circulation that mainly affects the Yangtze River Delta region into nine types. Local wind fields for the four industrial cities were classified according to their propensity for ventilation, stagnation, and recirculation based on the Allwine and Whiteman method. Based on the analysis of large-scale atmospheric circulation, we concluded that certain circulation patterns correspond to excessive ozone concentrations, while other circulation patterns correspond to good air quality. Moreover, ozone pollution was not closely related to local regional transmission. The importance of high temperatures in potentiating ozone pollution was also identified in the study area, whereas the effect of relative humidity was negligible. Finally, the importance of the different scale atmospheric motions was analyzed by studying two specific ozone pollution events in Xuzhou area (March, 2019) and Nanjing area (July-August, 2017). This analysis was complemented by HYSPLIT model's outputs to simulate the pollutant diffusion path. Regarding the first episode, ozone concentration is often closely related to the slowly approaching thermal high-pressure system. In the second episode, local transmission had little effect on the generation and spread of ozone pollution. Furthermore, and comparing the circulation conditions with local meteorological factors, it was found that the increase in ozone concentration was often accompanied by higher temperature, and the response to humidity was not clear.

Aldehydes in Relation to Air Pollution Sources: A Case Study around the Beijing Olympics

This study was carried out to characterize three aldehydes of health concern (formaldehyde, acetaldehyde, and acrolein) at a central Beijing site in the summer and early fall of 2008 (from June to October). Aldehydes in polluted atmospheres come from both primary and secondary sources, which limits the control strategies for these reactive compounds. Measurements were made before, during, and after the Beijing Olympics to examine whether the dramatic air pollution control measures implemented during the Olympics had an impact on concentrations of the three aldehydes and their underlying primary and secondary sources. Average concentrations of formaldehyde, acetaldehyde and acrolein were 29.3±15.1 μg/m3, 27.1±15.7 μg/m3 and 2.3±1.0 μg/m3, respectively, for the entire period of measurements, all being at the high end of concentration ranges measured in cities around the world in photochemical smog seasons. Formaldehyde and acrolein increased during the pollution control period compared to the pre-Olympic Games, followed the changing pattern of temperature, and were significantly correlated with ozone and with a secondary formation factor identified by principal component analysis (PCA). In contrast, acetaldehyde had a reduction in mean concentration during the Olympic air pollution control period compared to the pre-Olympic period and was significantly correlated with several pollutants emitted from local emission sources (e.g., NO2, CO, and PM2.5). Acetaldehyde was also more strongly associated with primary emission sources including vegetative burning and oil combustion factors identified through the PCA. All three aldehydes were lower during the post-Olympic sampling period compared to the before and during Olympic periods, likely due to seasonal and regional effects. Our findings point to the complexity of source control strategies for secondary pollutants.

An application of artificial neural network models to estimate air temperature data in areas with sparse network of meteorological stations

In this work artificial neural network (ANN) models are developed to estimate meteorological data values in areas with sparse meteorological stations. A more traditional interpolation model (multiple regression model, MLR) is also used to compare model results and performance. The application site is a canyon in a National Forest located in southern Greece. Four meteorological stations were established in the canyon; the models were then applied to estimate air temperature values as a function of the corresponding values of one or more reference stations. The evaluation of the ANN model results showed that fair to very good air temperature estimations may be achieved depending on the number of the meteorological stations used as reference stations. In addition, the ANN model was found to have better performance than the MLR model: mean absolute error values were found to be in the range 0.82-1.72 degrees C and 0.90-1.81 degrees C, for the ANN and the MLR models, respectively. These results indicate that ANN models may provide advantages over more traditional models or methods for temperature and other data estimations in areas where meteorological stations are sparse; they may be adopted, therefore, as an important component in various environmental modeling and management studies.

Determinants of ultrafine particle exposures in transportation environments: findings of an 8-month survey conducted in Montréal, Canada

An 8-month sampling campaign was conducted in Montréal, Canada to explore determinants of ultrafine particle (UFP) exposures in transportation environments and to develop models to predict such exposures. Between April and November 2006, UFP (0.02-1 mum) count exposure data were collected for one researcher during 80 morning and evening commutes including a 0.5-km walk, a 3-km bus ride, and a 26-km automobile ride in each direction. Ambient temperature, relative humidity, precipitation, and wind speed/direction data were collected for each transit period and the positions of bus and automobile windows were recorded. Mixing heights were also estimated. Morning UFP exposures were significantly greater than those in the evening, with the highest levels observed in the automobile and the lowest while walking. Wind speed and mixing height were highly correlated, and as a result only wind speed was considered in multivariable models owing to the accessibility of quantitative hourly monitoring data. In these models, each 10 degrees C increase in morning temperature was associated with decreases of 14,560/cm(3) (95% CI=11,111 to 18,020), 8160/cm(3) (95% CI=5060 to 11,260), and 11,310/cm(3) (95% CI=6820 to 15,810) for UFP exposures in walk, bus, and automobile environments, respectively. Likewise, each 10-km/h increase in morning wind speed corresponded to decreases of 8252/cm(3) (95% CI=5130 to 11,360), 6210/cm(3) (95% CI=3420 to 9000), and 6350/cm(3) (95% CI=2440 to 10,260) for UFP exposures in walk, bus, and automobile environments, respectively. Similar trends were observed in the evening hours. In an evaluation of model performance, moderate correlations were observed between measured and predicted UFP exposures on new bus (r=0.65) and automobile (r=0.77) routes. Further research is required to incorporate variables such as traffic density and vehicle ventilation settings into the models presented.