Chemical Mass Balance model applied to an olfactory annoyance problematic

Vehicular non-exhaust particulate emissions in Chinese megacities: Source profiles, real-world emission factors, and inventories

Vehicular non-exhaust emissions account for a significant share of atmospheric particulate matter (PM) pollution, but few studies have successfully quantified the contribution of non-exhaust emissions via real-world measurements. Here, we conduct a comprehensive study combining tunnel measurements, laboratory dynamometer and resuspension experiments, and chemical mass balance modeling to obtain source profiles, real-world emission factors (EFs), and inventories of vehicular non-exhaust PM emissions in Chinese megacities. The average vehicular PM_2.5 and PM₁₀ EFs measured in the four tunnels in four megacities (i.e., Beijing, Tianjin, Zhengzhou, and Qingdao) range from 8.8 to 16.0 mg km^-1 veh^-1 and from 37.4 to 63.9 mg km^-1 veh^-1, respectively. A two-step source apportionment is performed with the information of key tracers and localized profiles of each exhaust and non-exhaust source. Results show that the reconstructed PM₁₀ emissions embody 51-64% soil and cement dust, 26-40% tailpipe exhaust, 7-9% tire wear, and 1-3% brake wear, while PM_2.5 emissions are mainly composed of 59-80% tailpipe exhaust, 11-31% soil and cement dust, 4-10% tire wear, and 1-5% brake wear. Fleet composition, road gradient, and pavement roughness are essential factors in determining on-road non-exhaust emissions. Based on the EFs and the results of source apportionment, we estimate that the road dust, tire wear, and brake wear emit 8.1, 2.5, and 0.8 Gg year^-1 PM_2.5 in China, respectively. Our study highlights the importance of non-exhaust emissions in China, which is essential to assess their impacts on air quality, human health, and climate and formulating effective controlling measures.

A review of AirQ Models and their applications for forecasting the air pollution health outcomes

Even though clean air is considered as a basic requirement for the maintenance of human health, air pollution continues to pose a significant health threat in developed and developing countries alike. Monitoring and modeling of classic and emerging pollutants is vital to our knowledge of health outcomes in exposed subjects and to our ability to predict them. The ability to anticipate and manage changes in atmospheric pollutant concentrations relies on an accurate representation of the chemical state of the atmosphere. The task of providing the best possible analysis of air pollution thus requires efficient computational tools enabling efficient integration of observational data into models. A number of air quality models have been developed and play an important role in air quality management. Even though a large number of air quality models have been discussed or applied, their heterogeneity makes it difficult to select one approach above the others. This paper provides a brief review on air quality models with respect to several aspects such as prediction of health effects.