Severity Analysis of Wildlife–Vehicle Crashes using Generalized Structural Equation Modeling

Each year, thousands of wildlife–vehicle crashes (WVCs) occur in North America with negative effects on wildlife welfare, human health, and the economy. Although previous studies have investigated factors related to WVC frequency, limited research has been conducted on factors affecting WVC severity. Using more than 10,000 WVCs occurring in the province of Saskatchewan (Canada), this study investigated the severity outcomes of WVCs and their influencing factors, using structural equation modeling (SEM) with generalized (ordered probit) links. Compared with traditional severity analysis techniques, SEM offers the added advantage of representing, estimating, and testing complex modeling structures that include both measured and latent (unmeasured) variables. Three latent variables were introduced in this study: driver’s speeding attitude (SA), driver’s visibility impairment (VI), and crash severity. Measured variables obtained from crash records were included in the SEM to define latent constructs, and the resulting network of relationships was tested. The results showed that crash data supported the model hypothesis well, and the measured/latent variables adequately predicted crash severity. Overall, SA and VI were demonstrated to positively affect crash severity with SA being the most influential factor. Moreover, it was demonstrated that road surface condition was the most influential factor of the SA measurement model, and weather condition was the most influential factor with respect to VI. Finally, a comparison between generalized SEM results and traditional crash severity modeling using ordered probit links was conducted. Similarities and differences between these two approaches were discussed at the end of the study.

Analysis and Modelling of PM2.5 Temporal and Spatial Behaviors in European Cities

Particulate matter with an aerodynamic diameter of less than 2.5 µm (PM2.5) is associated with adverse effects on human health (e.g., fatal cardiovascular and respiratory diseases), and environmental concerns (e.g., visibility impairment and damage in ecosystems). This study aimed to evaluate temporal and spatial trends and behaviors of PM2.5 concentrations in different European locations. Statistical threshold models using Artificial Neural Networks (ANN) defined by Genetic Algorithms (GA) were also applied for an urban centre site in Istanbul, to evaluate the influence of meteorological variables and PM10 concentrations on PM2.5 concentrations. Lower PM2.5 concentrations were observed in northern Europe. The highest values were found at traffic-related sites. PM2.5 concentrations were usually higher during the winter and tended to present strong increases during rush hours. PM2.5/PM10 ratios were slightly higher at background sites and the lower values were found in northern Europe (Helsinki and Stockholm). Ratios were usually higher during cold months and during the night. The statistical model (ANN + GA) allowed evaluating the combined effect of different explanatory variables (temperature, wind speed, relative humidity, air pressure and PM10 concentrations) on PM2.5 concentrations, under different regimes defined by relative humidity (threshold value of 79.1%). Important information about the temporal and spatial trends and behaviors related to PM2.5 concentrations in different European locations was developed.

Characterization of black carbon-containing fine particles in Beijing during wintertime

Refractory black carbon (BC) is a product of incomplete combustion of fossil fuel, biomass and biofuel, etc. By mixing with other species, BC can play significant roles in climate change, visibility impairment and human health. Such BC-containing particles in densely populated megacities like Beijing may have specific sources and properties that are important to haze formation and air quality. In this work, we exclusively characterized the BC-containing particles in urban Beijing by using a laser-only Aerodyne soot particle aerosol mass spectrometer (SP-AMS), as part of the Atmospheric Pollution & Human Health (APHH) 2016 winter campaign. The average mass ratio of coating to BC core (RBC) was found to be ∼5.0. Positive matrix factorization shows the presence of significant primary fossil fuel and biomass-burning organics (64 % of total organics). Yet secondary species, including sulfate, nitrate and oxygenated organic aerosol (OA) species, could have significant impacts on the properties of BC-containing particles, especially for ones with larger BC core sizes and thicker coatings. Analyses of sources and diurnal cycles of organic coating reveal significant afternoon photochemical production of secondary OA (SOA), as well as nighttime aqueous production of a portion of highly oxygenated OA. Besides SOA, photochemical production of nitrate, not sulfate, appeared to be important. Further investigations on BC-containing particles during different periods show that, on average, more polluted periods would have more contributions from secondary species and more thickly coated BC tended to associate with more secondary species, indicating the important role of chemical aging to the pollution of BC-containing particles in urban Beijing during wintertime. However, for individual pollution events, primary species (fossil fuel, coal and biomass-burning emissions) could also play a dominant role, as revealed by the compositions of BC-containing particles in two polluted episodes during the sampling period.

Characteristics of PM2.5 Chemical Compositions and Their Effect on Atmospheric Visibility in Urban Beijing, China during the Heating Season

Beijing, which is the capital of China, suffers from severe Fine Particles (PM2.5) pollution during the heating season. In order to take measures to control the PM2.5 pollution and improve the atmospheric environmental quality, daily PM2.5 samples were collected at an urban site from 15 November to 31 December 2016, characteristics of PM2.5 chemical compositions and their effect on atmospheric visibility were analyzed. It was found that the daily average mass concentrations of PM2.5 ranged from 7.64 to 383.00 μg m−3, with an average concentration of 114.17 μg m−3. On average, the Organic Carbon (OC) and Elemental Carbon (EC) contributed 21.39% and 5.21% to PM2.5, respectively. Secondary inorganic ions (SNA: SO42− + NO3− + NH4+) dominated the Water-Soluble Inorganic Ions (WSIIs) and they accounted for 47.09% of PM2.5. The mass concentrations of NH4+, NO3− and SO42− during the highly polluted period were 8.08, 8.88 and 6.85 times greater, respectively, than during the clean period, which contributed most to the serious PM2.5 pollution through the secondary transformation of NO2, SO2 and NH3. During the highly polluted period, NH4NO3 contributed most to the reconstruction extinction coefficient (b′ext), accounting for 35.7%, followed by (NH4)2SO4 (34.44%) and Organic Matter (OM: 15.24%). The acidity of PM2.5 in Beijing was weakly acid. Acidity of PM2.5 and relatively high humidity could aggravate PM2.5 pollution and visibility impairment by promoting the generation of secondary aerosol. Local motor vehicles contributed the most to NO3−, OC, and visibility impairment in urban Beijing. Other sources of pollution in the area surrounding urban Beijing, including coal burning, agricultural sources, and industrial sources in the Hebei, Shandong, and Henan provinces, released large amounts of SO2, NH3, and NO2. These, which were transformed into SO42−, NH4+, and NO3− during the transmission process, respectively, and had a great impact on atmospheric visibility impairment.

Characterization of black carbon-containing fine particles in Beijing during wintertime

Refractory black carbon (BC) is a product from incomplete combustion of fossil fuel, biomass and biofuel, etc. By mixing with other species, BC can play significant roles in climate change, visibility impairment and human health. Such BC-containing particles in very densely-populated megacities, like Beijing, may have specific sources and properties, that are very important to the haze formation and air quality. In this work, we characterized exclusively the BC-containing particles only in urban Beijing, by using a laser-only Aerodyne soot particle aerosol mass spectrometer (SP-AMS), as a part of the Air Pollution and Human Health (APHH) 2016 winter campaign. The average mass ratio of coating-to-BC (RBC) was found to be ~ 5.0, much smaller than those of highly aged BC, indicating dominant contributions from primary emissions. Positive matrix factorization indeed shows the dominance of fossil fuel and biomass burning organics. Yet secondary species, including both sulfate, nitrate and oxygenated organic aerosol (OA) species, could have significant impacts on the properties of BC-containing particles, especially for ones with larger BC core sizes and thicker coatings. Analyses of the sources and diurnal cycles of organic coating reveal significant afternoon photochemical production of secondary OA (SOA), as well as the nighttime production of a portion of highly oxygenated OA. Besides SOA, photochemical production of nitrate, not sulfate, was very important. Further investigations on BC-containing particles at different periods show that, on average, more polluted periods would have more contributions from secondary species, and more thickly coated BC tended to associate with more secondary species, indicating the important role of chemical aging to the air pollution in urban Beijing during wintertime. However, for individual pollution events, aqueous-phase production of sulfate, nitrate and SOA might aggravate the pollution obviously under high relative humidity conditions, while sometimes local primary emissions (coal and biomass burning) could lead to serious and extremely polluted event too.