A multiple linear regression model with multiplicative log-normal error term for atmospheric concentration data.

The homoscedasticity assumption (the variance of the error term is the same across all the observations) is a key assumption in the ordinary linear squares (OLS) solution of a linear regression model. The validity of this assumption is examined for a multiple linear regression model used to determine the source contributions to the observed black carbon concentrations at 12 background monitoring sites across China using a hybrid modeling approach. Residual analysis from the traditional OLS method, which assumes that the error term is additive and normally distributed with a mean of zero, shows pronounced heteroscedasticity based on the Breusch-Pagan test for 11 datasets. Noticing that the atmospheric black carbon data are log-normally distributed, we make a new assumption that the error terms are multiplicative and log-normally distributed. When the coefficients of the multilinear regression model are determined using the maximum likelihood estimation (MLE), the distribution of the residuals in 8 out of the 12 datasets is in good accordance with the revised assumption. Furthermore, the MLE computation under this novel assumption could be proved mathematically identical to minimizing a log-scale objective function, which considerably reduces the complexity in the MLE calculation. The new method is further demonstrated to have clear advantages in numerical simulation experiments of a 5-variable multiple linear regression model using synthesized data with prescribed coefficients and lognormally distributed multiplicative errors. Under all 9 simulation scenarios, the new method yields the most accurate estimations of the regression coefficients and has significantly higher coverage probability (on average, 95% for all five coefficients) than OLS (79%) and weighted least squares (WLS, 72%) methods.

The carbonaceous aerosol levels still remain a challenge in the Beijing-Tianjin-Hebei region of China: Insights from continuous high temporal resolution measurements in multiple cities.

Carbonaceous aerosols in high emission areas attract worldwide attention of the scientific community and the public due to their adverse impacts on the environment, human health and climate. However, long-term continuous hourly measurements are scarce on the regional scale. In this study, a one-year hourly measurement (from December 1, 2016 to November 30, 2017) of organic carbon (OC) and elemental carbon (EC) in airborne fine particles was performed using semi-continuous OC/EC analyzers in Beijing, Tianjin, Shijiazhuang and Tangshan in the Beijing-Tianjin-Hebei (BTH) region in China, which is one of high emission areas in China, even in the world. Marked spatiotemporal variations were observed. The highest concentrations of OC (22.8 ±â€¯30.6 µg/m3) and EC (5.4 ±â€¯6.5 µg/m3) occurred in Shijiangzhuang while the lowest concentrations of OC (11.0 ±â€¯10.7 µg/m3) and EC (3.1 ±â€¯3.6 µg/m3) were obtained in Beijing and Tianjin, respectively. Pronounced monthly, seasonal and diurnal variations of OC and EC were recorded. Compared to published data from the past two decades for the BTH region, our OC and EC levels were lower, implying some effect of recent measures for improving the air quality. Significant correlations of OC versus EC (p < 0.001) were found throughout the study period with high slopes and correlation coefficients in winter, but low slopes and correlation coefficients in summer. The estimated secondary OC (SOC), based on the minimum R squared (MRS) method, represented 29%, 47%, 38% and 48% of the OC for Beijing, Tianjin, Shijiazhuang and Tangshan, respectively. These percentages are larger than previous ones obtained for the BTH region in the past decade. There were obvious differences in the potential source regions of OC and EC among the four cities. Obvious prominent potential source areas of OC and EC were observed for Beijing, which were mainly located in the central and western areas of Inner Mongolia and even extended to the Mongolian regions, which is different from the findings in previous studies. For all sites, adjacent areas of the main provinces in northern China were found to be important potential source areas.

Regionalization based on spatial and seasonal variation in ground-level ozone concentrations across China.

Owing to the vast territory of China and strong regional characteristic of ozone pollution, it's desirable for policy makers to have a targeted and prioritized regulation and ozone pollution control strategy in China based on scientific evidences. It's important to assess its current pollution status as well as spatial and temporal variation patterns across China. Recent advances of national monitoring networks provide an opportunity to insight the actions of ozone pollution. Here, we present rotated empirical orthogonal function (REOF) analysis that was used on studying the spatiotemporal characteristics of daily ozone concentrations. Based on results of REOF analysis in pollution seasons for 3years' observations, twelve regions with clear patterns were identified in China. The patterns of temporal variation of ozone in each region were separated well and different from each other, reflecting local meteorological, photochemical or pollution features. A rising trend in annual averaged Eight-hour Average Ozone Concentrations (O3-8hr) from 2014 to 2016 was observed for all regions, except for the Tibetan Plateau. The mean values of annual and 90 percentile concentrations for all 338 cities were 82.6±14.6 and 133.9±25.8µg/m3, respectively, in 2015. The regionalization results of ozone were found to be influenced greatly by terrain features, indicating significant terrain and landform effects on ozone spatial correlations. Among 12 regions, North China Plain, Huanghuai Plain, Central Yangtze River Plain, Pearl River Delta and Sichuan Basin were realized as priority regions for mitigation strategies, due to their higher ozone concentrations and dense population.