Using Statistical Regressions to Identify Factors Influencing PM_2.5Concentrations: The Pittsburgh Supersite as a Case Study

Abstract

Using data from the Pittsburgh Air Quality Study, we find that temperature, relative humidity, their squared terms, and their interactions explain much of the variation in airborne concentrations of PM 2.5 in the city. Factors that do not appreciably influence the concentrations over a full year include wind direction, inverse mixing height, UV radiation, SO 2 , O 3 , and season of the year. Comparison with similar studies of PM 2.5 in other cities suggests that the relative importance of different factors can vary greatly. Temperature and relative humidity are important factors in both Pittsburgh and New York City, and synoptic scale meteorology influencing these two sites can explain much of the pattern in PM 2.5 concentrations which peak in the summer. However, PM 2.5 levels in other cities have different seasonal patterns and are affected by a number of other factors, and thus the results presented here cannot be generalized to other locations without additional study.

Time series forecasting of methane concentrations in the surface layer of atmospheric air in Arctic region

“…Climate change particularly affects the Arctic regions. So in recent years, the temperature in some areas of the Russian Arctic at 6-7 ° C exceeded the average long-term observations [1], [2]. For this phenomenon (rapid climate change in the Arctic) has its name -"Arctic reinforcement" [3].…”

Two-step combined algorithm for improving the accuracy of predicting methane concentration in atmospheric air based on the NARX neural network and subsequent prediction of residuals

“…Для предсказания возможных климатических эффектов при изменении состава атмосферного воздуха, в частности концентрации парниковых газов, востребованы прогнозы, которые осуществляются с использованием классических статистических подходов [8][9][10] и климатических моделей [11]. Однако зачастую такие прогнозы недостаточно точны, так как набор факторов, влияющих на конечный результат, велик и неопределенен.…”

Conjoint approach of the "residual" prediction and the nonlinear autoregressive neural network increases the forecast precision of the base model

“…[1]- [2]. Forecasting the dynamics of atmospheric air pollution is carried out both with using of classical statistical approaches [3]- [8] and models based on artificial neural networks, which have become particularly popular in recent years [9]- [16]. Among the many types of artificial neural networks with time series prediction problems the Nonlinear Autoregressive Neural Network with an External Input (NARX) network is the most suitable [17]- [20].…”