Urban buses have energy and environmental impacts because they are mostly equipped with heavy-duty diesel engines, having higher emission factors and pollution levels. This study proposed a mean distribution deviation (MDD) method to identify bus pollutant emissions including CO, CO 2 , HC, and NO X at road sections, intersections, and bus stops for different fuel types; and explore the changes in emissions for different locations in the road sections, bus stops, and intersection influence areas. Bus speed, acceleration, and emissions data were collected from four fuel types in China. For different locations and fuel types, the differences in emissions were all statistically significant. MDD values for different locations indicated that there were more obvious differences in emissions between road sections and intersections. In addition, heat maps were applied in this study to better understand changes in bus emissions for different locations in the bus stop influence areas, intersection influence areas, and road sections. the proportion of new energy vehicles in buses is largely increasing, it is necessary to re-estimate the emissions characteristics of public transport vehicles, especially for new energy buses. In particular, intersection and bus stop areas can create congestion and further increase emission rates due to speed changes and stop-and-go operations [24][25][26][27]. In light of these considerations, measuring and comparing bus emissions at different locations are one of the emphases of this study.The first category of vehicle emission analysis methods is aimed at analyzing exhaust fumes for large areas and regions. This kind of emission analysis is regarded as a "macro" scale and is combined with the average values of influencing variables [28]. The second category aims to develop models to provide more precise knowledge about vehicle emissions in easily identifiable environments, which are called "micro" or "local" scale models. This approach offers the chance to acquire vehicle emissions data in real-world conditions [28,29]. Zhang et al. investigated the emission characteristics of transportation vehicles using real data [30]. From the results, the problem of NO X emissions is still confronted with severe challenges. For different locations, the performance of pollutant emissions may vary. In the relevant reference [18], approximately half of bus emissions were produced in and around intersection/stop areas. In addition, the use of alternative fuels, such as compressed natural gas, biodiesel, and hybrid technology (diesel/electric) reduced carbon emissions, leading to improved air quality [31][32][33][34]. For instance, Dreier et al. found an absolute reduction of 1115 g CO 2 per kilometer when using plug-in hybrid-electric city buses [35]. It should be noted that if the available data and information about the application system and technologies (such as bus emissions) are incomplete, imprecise, and uncertain, then technology identification could be very difficult. Liu et al. introduced a simpl...
Objectives To evaluate the prevalence and characteristics of doravirine resistance and cross-resistance in patients who failed first-line ART in China. Methods From 2014 to 2108, 4132 patients from five provinces were tested for drug resistance by genotypic resistance testing. Drug resistance mutations were assessed using the Stanford HIVdb algorithm Version 9.0. Sequences classified as having low-level, intermediate and high-level resistance were defined as having drug resistance. Results Overall, the prevalence of doravirine and other NNRTIs cross-resistance was 69.5%, with intermediate and high-level resistance accounting for 56.4%. Doravirine resistance highly correlated with efavirenz (r = 0.720) and nevirapine (r = 0.721) resistance and moderately correlated with etravirine (r = 0.637) and rilpivirine (r = 0.692) resistance. The most frequent doravirine-associated resistance mutations were V106M (8.7%), K101E (6.8%) and P225H (5.1%). High-level resistance was mainly due to Y188L (3.2%) and M230L (2.7%). There were significant differences between genotypes and provinces. Compared with CRF01_AE, CRF07_BC (OR = 0.595, 95% CI = 0.546–0.648) and CRF08_BC (OR = 0.467, 95% CI = 0.407–0.536) were associated with lower risks of doravirine resistance. Conversely, genotype A (OR = 3.003, 95% CI = 1.806–4.991) and genotype B (OR = 1.250, 95% CI = 1.021–1.531) were associated with higher risks of doravirine resistance. The risk of doravirine resistance was significantly lower in Xinjiang compared with other provinces. Conclusions In China, the prevalence of doravirine cross-resistance among patients who have failed first-line ART is high. Therefore, doravirine should not be used blindly without genotypic resistance testing and is not recommended for people who have failed first-line NNRTI-based ART.
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