Assessing the co-benefits of reductions in mobile-source CO2 and pollutant emissions for urban air qualityand public health.

The flood of economic information from China in recent years has permitted reassessment of the "Chinese model of development" and the extent to which China's own development experience has conformed to it. The prevailing consensus of the 1970s concerning the substance of the model itself-typically characterized in terms of the Chinese leadership's objectives and priorities and its strategy for pursuing them-has stood up quite well. The same cannot be said about past appraisals of the extent to which China's economic reality has been reshaped to accord with the model's prescriptions. One of the most widely remarked elements of the Chinese model is the high priority assigned to regional objectives and, more specifically, to "balance" in the distribution of productive capacity and equity in the distribution of income and, hence, consumption. During the Maoist era (i.e., prior to 1979), the central government pursued regional objectives through such measures as interregional transfers of investment resources (effected via the planning system), subsidization of health and education expenditures in poor regions, and attempts to maintain a safety net of state relief (effected largely via control of grain surpluses). In short, there is little doubt that prior to 1979 the Chinese leadership placed an unusually high value on the spread of modern growth and the improvements in welfare associated with it and persistently acted to limit the emergence or widening of interregional disparities.' Although China's post-Mao leaders have shown greater concern with efficiency and with the potential trade-offs between efficiency and such goals as balance and equity, their continuation of many redistributive policies indicates that they have not abandoned regional objectives. But how successful has China been in achieving these objectives?

Recent advances and perspectives towards emission inventories of mobile sources: Compilation approaches, data acquisition methods, and case studies

Based on the comprehensive development of the emission inventory of air pollution sources, the emission inventory of self-owned mobile sources of Tianjin coastal ports was researched and formulated. In this study, a gridded emission inventory with a resolution of 3 km×3 km was established for six types of air pollutants from road and non-road mobile sources. The spatial and temporal distribution characteristics of pollutant emissions were analyzed, and the uncertainty of the inventory was analyzed using the Monte Carlo method. The results showed that in 2020, the self-owned mobile sources of coastal ports emitted 148.22 t PM10, 135.34 t PM2.5, 1061.04 t SO2, 4027.16 t NOx, 756.60 t CO, and 237.07 t VOCs, of which the total emissions of road and non-road mobile sources accounted for 6.66% and 93.34% of the mobile source emissions, respectively. The main contributors to motor vehicle pollutant emissions from road mobile sources in the whole port area were small, medium, and large passenger vehicles (gasoline) and heavy trucks (diesel). The main contributors to the pollutants emitted by non-road mobile sources were ships and construction machinery. Uncertainty analysis results showed that the overall uncertainty of mobile sources ranged from -13.3% to 16.53%.

A six-day tunnel field study was conducted in the city of Monterrey, Mexico, during June 2009 to derive on-road emission factors (EFs) for trace gases and fine particulate matter from the local vehicle fleet. The Loma Larga Tunnel (LLT) is a 532-m-long structure that is mainly used by light-duty gasoline-powered vehicles. It is composed of two independent bores that have a semicircular cross section, 17 m in diameter with a 3.5% slope. During the study, a fleet of 108,569 vehicles with average speeds that ranged from 43 to 76 km/hr was sampled. Ambient air samples were taken inside each bore using 6-L SUMMA-polished canisters and low-volume samplers for the quantification of total nonmethane hydrocarbons (TNMHC) and PM2.5, respectively. The effect of road dust resuspension was considered in the computation of PM2.5 EFs. Additional equipment was used to measure real-time levels of CO2 and NOx; CO EFs were estimated using NOx as a surrogate. TNMHC samples and NOx levels were obtained for 2-hr time periods, while PM2.5 samples and CO2 levels were obtained using 2.5-hr time periods, which included the time periods of the TNMHC and NOx measurements. Estimated EFs for TNMHC, CO, NOx, and PM2.5 were 1.16 ± 0.05, 4.83 ± 2.9, and 0.11 ± 0.07 g/km-veh (2-hr average) and 17.5 ± 5.7 mg/veh-km (2.5-hr average), respectively, while CO2 EFs were 182.7 ± 44 g/km-veh for the 2-hr time periods and 170 ± 22 g/veh-km for the 2.5-hr time periods. The average fuel economy estimated from the field data was 12.3 ± 2.3 km/L. The CO2 and TNMHC EFs (on a mass per distance basis) tended to be higher for traffic moving upslope, while the inverse occurred for the PM2.5 EFs. In comparison to other tunnel studies, the CO2 EFs obtained were similar, the NOx and PM2.5 EFs were lower, and the CO and TNMHC EFs were higher. Implications Mobile source emission factors (EFs) for Mexican cities other than Mexico City are scarce. In Monterrey, Mexico, one of the three major cities in the country, emissions inventories are constructed based on EFs from other locations. However, it is quite relevant to obtain local information to construct reliable inventories. We present what is, to our knowledge, the first tunnel study conducted in a Mexican city other than Mexico City to estimate fleet-average mobile source EFs. This is also the first study that reports PM2.5 EFs derived from a tunnel study in the country.

Emissions inventories of fine particulate matter (PM2.5) were compared with estimates of emissions based on data emerging from U.S. Environment Protection Agency Particulate Matter Supersites and other field programs. Six source categories for PM2.5 emissions were reviewed: on-road mobile sources, nonroad mobile sources, cooking, biomass combustion, fugitive dust, and stationary sources. Ammonia emissions from all of the source categories were also examined. Regional emissions inventories of PM in the exhaust from on-road and nonroad sources were generally consistent with ambient observations, though uncertainties in some emission factors were twice as large as the emission factors. In contrast, emissions inventories of road dust were up to an order of magnitude larger than ambient observations, and estimated brake wear and tire dust emissions were half as large as ambient observations in urban areas. Although comprehensive nationwide emissions inventories of PM2.5 from cooking sources and biomass burning are not yet available, observational data in urban areas suggest that cooking sources account for approximately 5–20% of total primary emissions (excluding dust), and biomass burning sources are highly dependent on region. Finally, relatively few observational data were available to assess the accuracy of emission estimates for stationary sources. Overall, the uncertainties in primary emissions for PM2.5 are substantial. Similar uncertainties exist for ammonia emissions. Because of these uncertainties, the design of PM2.5 control strategies should be based on inventories that have been refined by a combination of bottom-up and top-down methods.

Brazil is currently facing a growing vehicle population and increasing concerns regarding air quality in large metropolitan areas such as Sao Paulo. Mobile source hydrocarbon emissions are a large contributor to ground level ozone and smog in many urban areas. In order to develop the most cost-effective and meaningful VOC control strategy, from both evaporative and exhaust, one needs to better understand the quantity currently not controlled and the cost impact to advance controls and capture of each. This paper will further evaluate the control opportunities and impacts to the overall goal of reducing mobile source hydrocarbon emissions and improving air quality. INTRODUCTION Brazil is currently facing a challenge regarding mobile source emissions. Vehicle sales year over year in Brazil continue to grow. According to IHS Automotive Scenarios Service, the projected vehicle sales will be 5.2 million by 2020, up over 40percent from 2012 [1]. This growth leads to increases in traffic, congestion, and pollution. In a 2011 study published by CETESB, passenger cars were the largest contributors to total hydrocarbon emissions in Sao Paulo [2]. Additionally in 2012, 98 days were measured where the national standard for ozone, 160 micrograms per cubic meter on a one-hour basis, was exceeded in Sao Paulo [3]. Possible contributors to this ozone formation were identified as the increase in pollutants from mobile sources – namely nitrogen oxides (NOx) and volatile organic compounds (VOC), which contribute to the creation of photochemical smog or ground level ozone.VOC levels must be reduced to reduce ozone. Currently PROCONVE addresses various pollutants with limits on key exhaust and evaporative emissions. Tailpipe emissions have received the majority of attention historically with specific focuses on NOx, sulfur dioxide (SO2), non-methane hydrocarbon (NMHC), particulate matter (PM) and carbon dioxide (CO2).This trend continues in PROCONVE L6 which contains exhaust emissions based on the United States Environmental Protection Agency’s (US EPA) 2004 Tier 2 Bin 7 requirements [4]. Evaporative emission standards lag further behind and currently match closest with the 1980 US EPA standard. There is still much opportunity with regards to VOC emissions controls from mobile sources in Brazil. 1. SOURCES OF MOBILE SOURCE VOC EMISSIONS There are many sources that vehicle engine is shut off and emission sources from a motor vehicle interior, and window washing fluid; however, these paper. Figure 1. Air induction system (AIS) losses occur when gasoline evaporates from the engine. occur from unburned fuel in the cylinders, intake manifold, leaking cylinders, and from the crankcase. AIS emissions are p uncontrolled emissions are approximately during the hot soak [5]. Currently, the low limit of the rig test and vehicle emissions standard requiredby the Air Resources Board (ARB) including partial zero emission vehicle ( vehicles. Evaporative gasoline can migrate through the use of ethanol in gasoline increases the amount of permeation. studies that include over 150 vehicles for permeation. These permeation uncontrolled VOC emissions [ As the vehicle is parked and exposed to daily temperature increases, diurnal tank venting losses occur. Daily evaporated gasoline from the fuel tank can result in day of uncontrolled emission. comprise vehicle emissions, some of which occur when the the vehicle is parked. Figure 1 identifies several of the . Other sources of emissions includ sources arenotdiscussed Major Sources of VOC Emissions rimarily from leaking injectors. It is estimated that these 0,1 grams per diurnal test plus addi in California necessitate control on some vehicles PZEV) vehicles and high performance LEV II plastic fuel system components. In some instances, US EPA reviewed from a range of model years to develop emission factors losses account for 0,01 to 0,311 grams per 6]. one to main e tires, vehicle in detail in this

BackgroundAir pollution is associated with increased risk of cognitive decline and dementia. However, whether and how air quality (AQ) improvement is associated with better cognitive performance across multiple domains is unknown.MethodParticipants included 2,114 women (aged 74‐92 at the enrollment) from the US‐based Women’s Health Initiative Memory Study‐Epidemiology of Cognitive Health Outcomes. Cognitive domains evaluated annually included episodic memory (EM; sum of scores by immediate and delayed recalls of East Boston Memory Test and the word list of modified Telephone Interview for Cognitive Status), language (Verbal Fluency‐Animals), working memory (WM, sum of scores from forward and backward Digit Span Tests), and attention/executive function (AEF, log‐transformed ratio of the time spent in Part A over Part B for Oral Trails Making Test), all standardized using corresponding baseline measure. Annual PM2.5 (fine particulate matter) and NO2 were estimated using regionalized universal kriging models and aggregated to 3‐year average at 10 years (remote) and immediately (recent) before enrollment. AQ improvement was defined as reduction from remote to recent exposures. We examined associations between AQ improvement and domain‐specific cognitive trajectories using linear mixed effect models, adjusting for sociodemographic, lifestyle, clinical covariates, and time‐varying propensity scores to account for selective attrition.ResultsAQ improved significantly over 10 years for both PM2.5 (13.3±2.7µg/m3 to 10.6±2.0µg/m3) and NO2 (15.7±7.1ppb to 10.4±4.9ppb). Over a median of 6.2 (inter‐quartile‐range [IQR]=5.1) years of follow‐up, cognitive performance declined significantly in all domains. Women residing in locations with greater PM2.5 improvement (per IQR=1.77µg/m3) had better performance in EM (βPM2.5=0.069, p=0.002), WM (βPM2.5=0.050, p=0.01), and AEF (βPM2.5=0.044, p=0.01), but not in language (βPM2.5=0.013, p=0.57). Similar findings with stronger associations were observed with improved NO2 (βNO2=0.065∼0.073 per IQR=3.89ppb for EM, WM and AEF, all p≤0.002). Slower declines in cognitive trajectories across EM, Language and AEF were found with improved AQ, but only the association between improved NO2 and slower EM decline was statistically significant (βNO2=0.010/year, p<0.001).ConclusionsImproved AQ of PM2.5 and NO2 was associated with better performance of EM, WM and AEF, but not language. In addition, improved NO2 was associated with slower decline of EM over time in older women.

Fuel consumption from vehicles of China until 2030 in energy scenarios

A fuel‐based approach is used to estimate exhaust emissions of nitrogen oxides (NOx) and fine particulate matter (PM2.5) from mobile sources in the United States for the years 1996–2006. Source categories considered include on‐road and off‐road gasoline and diesel engines. Pollutant emissions for each mobile source category were estimated by combining fuel consumption with emission factors expressed per unit of fuel burned. Over the 10‐year time period that is the focus of this study, sales of gasoline and diesel fuel intended for on‐road use increased by 15 and 43%, respectively. Diesel fuel use by off‐road equipment increased by ∼20% over the same time period. Growth in fuel consumption offset some of the reductions in pollutant emission factors that occurred during this period. For NOx, there have been dramatic (factor of 2) decreases in emission factors for on‐road gasoline engines between 1996 and 2006. In contrast, diesel NOx emission factors decreased more gradually. Exhaust PM2.5 emission factors appear to have decreased for most engine categories, but emission uncertainties are large for this pollutant. Diesel engines appear to be the dominant mobile source of both NOx and PM2.5; the diesel share of total NOx has increased over time as gasoline engine emissions have declined. Comparing fuel‐based emission estimates with U.S. Environmental Protection Agency's national emission inventory led to the following conclusions: (1) total emissions of NOx and PM2.5 estimated by two different methods were similar, (2) source contributions to these totals differ significantly, with higher relative contributions coming from on‐road diesel engines in this study.

Speciated fine particulate matter (PM2.5) data collected as part of the Speciation Trends Network at four sites in the Midwest (Detroit, MI; Cincinnati, OH; Indianapolis, IN; and Northbrook, IL) and as part of the Interagency Monitoring of Protected Visual Environments program at the rural Bondville, IL, site were analyzed to understand sources contributing to organic carbon (OC) and PM2.5 mass. Positive matrix factorization (PMF) was applied to available data collected from January 2002 through March 2005, and seven to nine factors were identified at each site. Common factors at all of the sites included mobile (gasoline)/secondary organic aerosols with high OC, diesel with a high elemental carbon/OC ratio (only at the urban sites), secondary sulfate, secondary nitrate, soil, and biomass burning. Identified industrial factors included copper smelting (North–brook, Indianapolis, and Bondville), steel/manufacturing with iron (Northbrook), industrial zinc (North–brook, Cincinnati, Indianapolis, and Detroit), metal plating with chromium and nickel (Detroit, Indianapolis, and Bondville), mixed industrial with copper and iron (Cincinnati), and limestone with calcium and iron (Bondville). PMF results, on average, accounted for 96% of the measured PM2.5 mass at each site; residuals were consistently within tolerance (±3), and goodness–of–fit (Q) was acceptable. Potential source contribution function analysis helped identify regional and local impacts of the identified source types. Secondary sulfate and soil factors showed regional characteristics at each site, whereas industrial sources typically appeared to be locally influenced. These regional factors contributed approximately one third of the total PM2.5 mass, on average, whereas local mobile and industrial sources contributed to the remaining mass. Mobile sources were a major contributor (55–76% at the urban sites) to OC mass, generally with at least twice as much mass from nondiesel sources as from diesel. Regional OC associated with secondary sulfate and soil was generally low.

Background:Reductions in ambient concentrations of fine particulate matter () have contributed to reductions in cardiovascular (CV) mortality.Objectives:We examined changes in CV mortality attributed to reductions in emissions from mobile, point, areal, and nonroad sources through changes in concentrations of and its major components [nitrates, sulfates, elemental carbon (EC), and organic carbon (OC)] in 2,132 U.S. counties between 1990 and 2010.Methods:Using Community Multiscale Air Quality model estimated total and component concentrations, we calculated population-weighted annual averages for each county. We estimated total- and component-related CV mortality, adjusted for county-level population characteristics and baseline concentrations. Using the index of Emission Mitigation Efficiency for primary emission-to-particle pathways, we expressed changes in particle-related mortality in terms of precursor emissions by each sector.Results: reductions represented 5.7% of the overall decline in CV mortality. Large point source emissions of sulfur dioxide accounted for 6.685 [95% confidence interval (CI): 5.703, 7.667] fewer sulfate-related CV deaths per 100,000 people. Mobile source emissions of primary EC and nitrous oxides accounted for 3.396 (95% CI: 2.772, 4.020) and 3.984 (95% CI: 2.472, 5.496) fewer CV deaths per 100,000 people respectively. Increased EC and OC emissions from areal sources increased carbon-related CV mortality by 0.788 (95% CI: , 2.116) and 0.245 (95% CI: , 1.187) CV deaths per 100,000 people.Discussion:In a nationwide epidemiological study of emission sector contribution to –related mortality, we found that reductions in sulfur-dioxide emissions from large point sources and nitrates and EC emissions from mobile sources contributed the largest reduction in particle-related mortality rates respectively. https://doi.org/10.1289/EHP5692

Anthropogenic emission inventory of multiple air pollutants and their spatiotemporal variations in 2017 for the Shandong Province, China

European survey for NO x emissions with emphasis on Eastern Europe

BackgroundThe phenomenon of urban-rural segmentation has emerged and is remarkable, and the health disparities between rural and urban China should be stressed.MethodsBased on data from the Chinese General Social Survey from 2005 to 2013, this study not only explored the net age, period, and cohort effects of self-rated health, but compared these effects between rural and urban China from a dynamic perspective through hierarchical age-period-cohort-cross-classified random effects model.ResultsUrban-rural disparities, as well as work status and gender disparities in health increased with age, in line with the cumulative advantage/disadvantage effects theory, while marital status disparities in health declining with age was in line with the age-as-leveler effects theory. The war cohort, famine cohort, later cultural revolution cohort, and early reform cohort had poorer health than did those in the early China cohort, economic recovery cohort, and later reform cohort. The economic crisis period, war cohort, baby boomer, and early cultural revolution cohort encountered larger urban-rural health disparities, while the early China cohort and early reform cohort experienced smaller urban-rural disparities in health.ConclusionsPopulation health is closely related to social context and health care development. It is necessary to keep economic development stable and boost medical technology improvements and the construction of the health care system.

Vehicle emissions from mobile sources are major contributors to air pollution and varied with vehicle types, vehicle styles, traveled miles, temperature, oil types and the methods of operation and management. This study performs three emission factor models, Mobile-Taiwan 2, Mobile6.2 and EFDB to calculate emission factor of mobile sources from year 1986 to 2011. The emissions of primary air pollutants, MIRs and CO2emitted from mobile sources were calculated. The contribution ratios of varied vehicle types for different air pollutants would be compared and analyzed. Estimated emissions from mobile sources were 32.2, 177, 643, 197 and 401 kilotons/y for PM10, NOx, CO, THC and MIR for 2000; 31.3, 115, 305, 114 and 227 kilotons/y for 2011. Emissions of traditional air pollutants presented a decreasing trend because of fourth-stage emission standards for mobiles sources and CO2 revealed an increasing trend. According to presented control technology for greenhouse gases on mobile sources, ratio of emission for year 2011 to 2000 would be 1.38-1.49.