Thirty‐four 10‐yr‐old white pines (Pinus strobus L.) growing on reclaimed minesoils in Virginia were selected to evaluate the effects of selected minesoil properties on tree growth. A 1‐m deep backhoe pit was dug at the base of each tree to determine rooting depth, and surface soil samples (0–10 cm) were collected for analysis of selected physical and chemical properties. Multiple regression analysis was used to model the combined effects of minesoil properties on tree height. The minesoil variable that had the greatest influence on tree growth was rooting volume index, defined as depth to a restrictive layer times the soil‐sized fraction (%) of the surface 10 cm. The next most influential minesoil property that affected height was soluble salt content; an inverse relationship existed between tree height and electrical conductivity of a 1:5 soil/water extract. A linear regression equation describing white pine site index (SI50) as a function of the square root of depth to a restrictive layer was highly significant.

PM2.5 is one of the major air pollutants in Kathmandu Valley, and its emission and the unique atmospheric condition of the valley make it significantly hazardous to human health. The air pollution due to particulate matter is a major health issue with numerous negative impacts on us. This research aims to quantify the health impacts of PM2.5 exposure on the population of Kathmandu Valley. The ambient PM2.5 concentration of Kathmandu Valley was simulated using WRF-Chem model by using a horizontal grid resolution of 3 × 3 km. The concentration obtained from WRF-Chem was used as input in the health equation of an intervention model to quantify the health impacts. This quantitative assessment of atmospheric pollution was applied to evaluate the human exposure to PM2.5 in Kathmandu Valley. PM2.5 concentration and its distribution in the valley along with the ward-wise population distribution were used to find the health impact of the particulate matter in December 2019 in Kathmandu Valley. Exposure analysis using the model showed that 19 people could die due to lung cancer and 175 people could die due to all cause diseases except accidents due to PM2.5 exposure in December 2019. It was estimated that the reduction in the PM2.5 level by half in the valley reduces the monthly mortality by 51.4%. Hence, the exposure analysis of the particulate matter on the urban population could be improved by using air quality models in order to solve the health problems arising from air pollution.