Spatiotemporal analysis of traffic emissions in over 5000 municipal districts in Brazil

Carbon Emission Measurement of Urban Green Passenger Transport: A Case Study of Qingdao

Urban passenger transport is one of the most significant sources of fossil energy consumption and greenhouse gas emission, especially in developing countries. The rapid growth of urban transport makes it a critical target for carbon reduction. This paper establishes a method for calculating carbon emission from urban passenger transport including ground buses, private cars, cruising taxis, online-hailing taxis, and rail transit. The scope of the study is determined according to the transportation mode and energy type, and the carbon emission factor of each energy source is also determined according to the local energy structure, etc. Taking into consideration the development trend of new energy vehicles, a combination of “top-down” and “bottom-up” approaches is used to estimate the carbon dioxide emission of each transportation mode. The results reveal that carbon emission from Qingdao’s passenger transport in 2020 was 8.15 million tons, of which 84.31% came from private cars, while the share of private cars of total travel was only 45.66%. Ground buses are the most efficient mode of transport. Fossil fuels emit more greenhouse gases than other clean energy sources. The emission intensity of hydrogen fuel cell buses is better than that of other fuel type vehicles. Battery electric buses have the largest sensitivity coefficient, therefore the carbon emission reduction potentially achieved by developing battery electric buses is most significant.

Top-down vehicle emission inventory for spatial distribution and dispersion modeling of particulate matter

Emission inventories are one of the most critical inputs for the successful modeling of air quality. The performance of the modeling results is directly affected by the quality of atmospheric emission inventories. Consequently, the development of representative inventories is always required. Due to the lack of regional inventories in Brazil, this study aimed to investigate the use of the particulate matter (PM) emission estimation from the Brazilian top-down vehicle emission inventory (VEI) of 2012 for air quality modeling. Here, we focus on road vehicles since they are usually responsible for significant emissions of PM in urban areas. The total Brazilian emission of PM (63,000 t year^-1) from vehicular sources was distributed into the urban areas of 5557 municipalities, with 1-km² grid spacing, considering two approaches: (i) population and (ii) fleet of each city. A comparison with some local inventories is discussed. The inventory was compiled in the PREP-CHEM-SRC processor tool. One-month modeling (August 2015) was performed with WRF-Chem for the four metropolitan areas of Brazilian Southeast: Belo Horizonte (MABH), Great Vitória (MAGV), Rio de Janeiro (MARJ), and São Paulo (MASP). In addition, modeling with the Emission Database for Global Atmospheric Research (EDGAR) inventory was carried out to compare the results. Overall, EDGAR inventory obtained higher PM emissions than the VEI segregated by population and fleet, which is expected owing to considerations of additional sources of emission (e.g., industrial and residential). This higher emission of EDGAR resulted in higher PM₁₀ and PM_2.5 concentrations, overestimating the observations in MASP, while the proposed inventory well represented the ambient concentrations, obtaining better statistics indices. For the other three metropolitan areas, both EDGAR and the VEI inventories obtained consistent results. Therefore, the present work endorses the fact that vehicles are responsible for the more substantial contribution to PM emissions in the studied urban areas. Furthermore, the use of VEI can be representative for modeling air quality in the future.

Ambient particulate matter pollution and adult hospital admissions for pneumonia in urban China: A national time series analysis for 2014 through 2017

BACKGROUND

The effects of ambient particulate matter (PM) pollution on pneumonia in adults are inconclusive, and few scientific data on a national scale have been generated in low- or middle-income countries, despite their much higher PM concentrations. We aimed to examine the association between PM levels and hospital admissions for pneumonia in Chinese adults.

METHODS AND FINDINGS

A nationwide time series study was conducted in China between 2014 and 2017. Information on daily hospital admissions for pneumonia for 2014-2017 was collected from the database of Urban Employee Basic Medical Insurance (UEBMI), which covers 282.93 million adults. Associations of PM concentrations and hospital admissions for pneumonia were estimated for each city using a quasi-Poisson regression model controlling for time trend, temperature, relative humidity, day of the week, and public holiday and then pooled by random-effects meta-analysis. Meta-regression models were used to investigate potential effect modifiers, including cities' annual-average air pollutants concentrations, temperature, relative humidity, gross domestic product (GDP) per capita, and coverage rates by the UEBMI. More than 4.2 million pneumonia admissions were identified in 184 Chinese cities during the study period. Short-term elevations in PM concentrations were associated with increased pneumonia admissions. At the national level, a 10-μg/m3 increase in 3-day moving average (lag 0-2) concentrations of PM2.5 (PM ≤2.5 μm in aerodynamic diameter) and PM10 (PM ≤10 μm in aerodynamic diameter) was associated with 0.31% (95% confidence interval [CI] 0.15%-0.46%, P < 0.001) and 0.19% (0.11%-0.30%, P < 0.001) increases in hospital admissions for pneumonia, respectively. The effects of PM10 were stronger in cities with higher temperatures (percentage increase, 0.031%; 95% CI 0.003%-0.058%; P = 0.026) and relative humidity (percentage increase, 0.011%; 95% CI 0%-0.022%; P = 0.045), as well as in the elderly (percentage increase, 0.10% [95% CI 0.02%-0.19%] for people aged 18-64 years versus 0.32% [95% CI 0.22%-0.39%] for people aged ≥75 years; P < 0.001). The main limitation of the present study was the unavailability of data on individual exposure to PM pollution.

CONCLUSIONS

Our findings suggest that there are significant short-term associations between ambient PM levels and increased hospital admissions for pneumonia in Chinese adults. These findings support the rationale that further limiting PM concentrations in China may be an effective strategy to reduce pneumonia-related hospital admissions.

Analysis of metal content and vertical stratification of epiphytic mosses along a Karst Mountain highway

Road-based transport emissions are a major source of atmospheric metal pollution. However, there have been few studies on emissions from road traffic in mountainous areas. In this study, epiphytic mosses from trees at different elevations of a highway, a typical road with extraordinary elevation change in a mountainous area of karst in Guizhou, China, were analyzed for metal content as well as the spatial distribution pattern of metals. Mosses were sampled from three sections of highway at different elevations, from 1292-1357, 1394-1441, to 1481-1548 m. Principal component analysis and heat-map clustering were used to identify the principal factors affecting metal deposition. The results show that the metals of mosses from different elevations were divided into four factors. Group 1 which included Ni, Fe, Mg, Ba, and Al was attributed to a dominantly geogenic source. Group 2 included Zn, Cu, Mn, and Cr, from vehicle-related materials including tires and brakes. Group 3, Cd, can be attributed to high Cd background levels from local origins and traffic emissions, particularly tire wear. Group 4, Pb, is associated with brake wear and historical deposition. The epiphytic moss widely distributed in the study area, Ectropothecium aneitense Broth. & Watts, was used to analyze the spatial distribution pattern of the metals. Metal content gradually decreased with increase in elevation. Levels of Ni, Fe, Mn, Ba, and Cd were all significantly correlated with elevation (p < 0.05), simultaneously affected by terrain and vertically stratified. We highlighted the vertical distribution characteristics of metal in epiphytic mosses in this study, which could improve moss application for ecological monitoring due to road-based transport emissions with elevation changes.