Detailed Activity Data Research Articles

Exploring emission spatiotemporal pattern and potential reduction capacity in China's aviation sector: Flight trajectory optimization perspective

China's rapid expansion of civil aviation has led to an increase in pollution-related issues, causing adverse health effects on populations near airports and downwind. Accurately quantifying aviation emissions is essential for effective emission management. Here, we developed a high-resolution aviation emissions inventory for China by employing a bottom-up approach that relied on daily flight schedules. By using the Aeronautical Information Publication (AIP) to reproduce real-world flight routes rather than conventional great-circle routes, we improved the accuracy of emissions and investigated the potential for reducing these emissions. Our findings demonstrated substantial variations in domestic civil aviation emissions both spatially and temporally. Emissions peaked in most provinces during Chinese holidays, particularly the Chinese Lunar New Year and summer holidays, highlighting the importance of detailed activity data for accurate emissions calculations. Therefore, we recommend extensive utilization of real-world flight routes, particularly in areas with limited Automatic Dependent Surveillance-Broadcast (ADS-B) coverage since they provide more accurate representations of actual flight trajectories. Our study also identified regions like Shaanxi, Sichuan, Beijing, and their surroundings having considerable potential for emission reduction due to substantial deviations from great-circle routes. This approach can enhance the accuracy and spatiotemporal resolution of aviation emissions at national and global scales throughout the year, without relying on extensive, long-term real-time flight trajectories. Additionally, it provides a unique way to quantify the potential for emission reductions across provinces in civil aviation, ultimately contributing to mitigating pollution-related health impacts from aviation emissions and promoting a more sustainable aviation industry.

Spatial-temporal characteristics of methane emission in shandong, China, based on an updated comprehensive methane emission inventory in 2020

As methane (CH4) is a powerful greenhouse gas (GHG) responsible for global warming, a refined CH4 emission inventory is crucial for formulating targeted mitigation policies and achieving carbon neutrality. However, provincial comprehensive CH4 emission inventories are rare due to data deficiency and fail to reflect the spatial and temporal characteristics of CH4 emission. Owing to its large population and rapid economic growth, Shandong Province has become the largest GHG emitter in China. Combining bottom-up and top-down methods, a high-spatial-resolution (0.03° × 0.03°) natural and anthropogenic CH4 emission inventory across Shandong Province in 2020 was established based on detailed activity data and updated source-specific emission factors. The total CH4 emission of Shandong was 1711.31 Gg in 2020, of which anthropogenic sources were responsible for 89.96% emission. The top three contributors, namely energy activities, agricultural activities, and waste treatment accounted for 52.86%, 21.25% and 12.70% emission, respectively, followed by vegetation (5.40%), wetland (4.64%), biomass burning (3.14%). Owing to the emission structure, spatial distribution of CH4 emission varied significantly. Jining was the largest contributor (488.92 Gg; 28.57% provincial CH4 emission) owing to the high coal production, followed by Heze (149.17 Gg; 8.72% provincial CH4 emission) and Dongying (138.28 Gg; 8.08% provincial CH4 emission) owing to coal and oil production. The monthly variation of CH4 emission from different sources are different, and the maximum emissions occurred in August. This study reports a set of methods for creating a CH4 emission inventory in China and provides detailed data for air quality simulations and various emission control policies.

Improvement in the Methodological Estimation of Sulfur Hexafluoride Use in Electrical Equipment for Malaysia’s National Greenhouse Gas Inventory

Sulfur hexafluoride (SF6) gas is one of the high global warming potential (GWP) gases regulated under the Kyoto Protocol. In Malaysia’s Biennial Update Report 3, the Revised 1996 Intergovernmental Panel on Climate Change Guidelines were followed to estimate the SF6 emissions in the country, including the time series from 1990 to 2016. The majority of SF6 emissions originate from the use of this gas in electrical equipment, where it is predominantly used in transmission switch gears, which have increased rapidly because of increasing electricity demand. SF6 gas plays a significant role in greenhouse gas emissions in Malaysia because this gas has a higher GWP than carbon dioxide. Hence, this SF6 gas needs to be estimated using newer guidelines such as the 2006 IPCC Guidelines. Detailed activity data in the new methodology require stakeholders’ engagement with utility providers and government agencies. This paper describes the GHG inventory improvement plan, focusing on SF6 emissions for Industrial Processes and Product Use for Malaysia through this transition of methodology.

Quantifying the impact of a smart farming system application on local-scale air quality of smallhold farms in Greece

Smart farming (SF) has emerged as a scientific approach exploiting technology advances for the management of agricultural practices, focusing on the control of resources and chemicals used. There is still limited evidence in the scientific literature in regard to the efficiency of SF, particularly for targeted environmental issues, such as air pollutant emissions from agricultural activities. The present paper expoits quantitative data collected from questionnaires to farmers of 6 pilot areas in Greece, participating in the LIFE GAIA Sense project. Emissions and pollutant levels were calculated for two consecutive years in these pilot areas, namely 2019 (baseline year) and 2020, which is the first SF application year. The methodology for calculating realistic emissions data, following a combined tier 1/tier2 approach is presented. To this purpose, detailed activity data of the specific SF application areas related to agricultural activities were acquired, based on the responses of participating farmers to targeted questionnaires. Calculated emissions were used as input data for air quality modeling simulations to examine the efficiency of SF in reducing local pollutant concentrations. The results show significant emissions and concentrations reductions in five out of the six pilot areas, for all pollutants and greenhouse gases studied, due to the decrease in fuel consumption and N fertilizer applied, as a result of the farmers following the SF advice. Particularly for NH3, which is an agricultural air pollutant of concern due to its health and environmental impacts, emission reductions of around 30% (and by up to almost 60%) were calculated.

County-level of particle and gases emission inventory for animal dung burning in the Qinghai–Tibetan Plateau, China

Emissions from animal dung burning in the Qinghai–Tibetan Plateau (QTP) influenced heavily on the regional and global climate and environment, from which the emission inventory of multiple pollutants is not yet available. This study developed a county-level emission inventory of particles and gases pollution based on field measured emission factors and detailed activity data for the QTP in 2019. The results revealed that the total emissions of particulate matter with an aerodynamic diameter of ≤2.5 μm (PM2.5), organic carbon (OC), elemental carbon (EC), eight ions, fifty-one polycyclic aromatic hydrocarbons (PAHs) in gaseous and particulate phases, 108 volatile organic compounds (VOCs), CO, and CO2 were 41.1, 23.1, 2.36, 3.15, 1.13, 6.55, 135, and 4963 Gg, respectively. Gonghe, Qilian, Nyima, and Gangcha Counties in northeast of Qinghai and northwestern Tibet were the major contributors of all pollutants contributed (accounting for 11.9%–14.6% of the total emission) due to their higher livestock numbers. Sheep dung burning contributed a little more (average of 67.1%) than yak dung burning (32.9%) due to the high EFs and consumptions. For temporal distribution, the heating season contributed majority of the total emissions (70%–82%). Although with much less population, the QTP showed even higher emission densities than those in plain areas due to less oxygen and poor combustion technology. Compared with existing emission inventories, the uncertainties of all pollutant emission inventories were considerably reduced (42.7–163%), with EC as an exception because of its high coefficients of variation of EFs than the other pollutants. The developed emission inventory can help formulate targeted air pollution control measures for the QTP region by providing detailed information on the spatiotemporal distribution of multitype air pollutants. Meanwhile, the relative high resolution and accuracy inventory could serve a better basis for atmospheric modeling such as pollution transportation and source apportionment in both regional and global scales.

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

Shandong is the most populous and highly industrialized province in eastern China, and the resultant poor air quality is a cause for widespread concern. This study combines bottom–up and top–down approaches to develop a high-resolution anthropogenic emission inventory of air pollutants for 2017. The inventory was developed based on updated emission factors and detailed activity data. The emissions of sulfur dioxide (SO2), nitrogen oxides (NOx), particulate matter with aerodynamic diameters smaller than 2.5 and 10 μm (PM2.5 and PM10, respectively), carbon monoxide (CO), volatile organic compounds (VOCs), and ammonia (NH3) were estimated to be 1387.8, 2488.6, 5281.7, 3193.0, 9250.7, 2254.7, and 1210.6 kt, respectively. Power plants were the largest contributors of SO2 and NOx emissions accounting for 43.7% and 41.9% of the total emissions, respectively. CO emissions mainly originated from industrial processes (40.1%), mobile sources (24.8%), and fossil fuel burning (21.2%). The major sources of PM10 and PM2.5 emissions were industrial processes and fugitive dust, contributing 83.0% and 86.9% of their total emissions, respectively. Industrial processes (60.0%) contributed the largest VOC emissions, followed by mobile sources (16.8%) and solvent use (14.5%). Livestock and N-fertilizers were major emitters of NH3, accounting for 69.9% and 21.2% of the total emissions, respectively. Emissions were spatially allocated to grid cells with a resolution of 0.05 ° × 0.05 ° based on spatial surrogates, using Geographic Information System (GIS). Heavy pollutant emissions were mainly concentrated in the central and eastern areas of Shandong, while high NH3–emissions occurred in the western region. Most pollutant emissions from industrial sectors occurred in June and July, while low emissions were recorded between January and February. Range uncertainties in emission inventory were quantified using Monte Carlo simulations. Our inventory provides effective information to understand local pollutant emission characteristics, perform air quality simulations, and formulate pollution control measures.

A comparative study of anthropogenic CH<sub>4</sub> emissions over China based on the ensembles of bottom-up inventories

Abstract. Atmospheric methane (CH4) is a potent greenhouse gas that is strongly influenced by several human activities. China, as one of the major agricultural and energy production countries, contributes considerably to the global anthropogenic CH4 emissions by rice cultivation, ruminant feeding, and coal production. Understanding the characteristics of China's CH4 emissions is necessary for interpreting source contributions and for further climate change mitigation. However, the scarcity of data from some sources or years and spatially explicit information pose great challenges to completing an analysis of CH4 emissions. This study provides a comprehensive comparison of China's anthropogenic CH4 emissions by synthesizing the most current and publicly available datasets (13 inventories). The results show that anthropogenic CH4 emissions differ widely among inventories, with values ranging from 44.4–57.5 Tg CH4 yr−1 in 2010. The discrepancy primarily resulted from the energy sector (27.3 %–60.0 % of total emissions), followed by the agricultural (26.9 %–50.8 %) and waste treatment (8.1 %–21.2 %) sectors. Temporally, emissions among inventories stabilized in the 1990s but increased significantly thereafter, with annual average growth rates (AAGRs) of 2.6 %–4.0 % during 2000–2010 but slower AAGRs of 0.5 %–2.2 % during 2011–2015, and the emissions became relatively stable, with AAGRs of 0.3 %–0.8 %, during 2015–2019 because of the stable emissions from the energy sector (mainly coal production). Spatially, there are large differences in emissions hotspot identification among inventories, and incomplete information on emission patterns may mislead or bias mitigation efforts for CH4 emission reductions. The availability of detailed activity data for sectors or subsectors and the use of region-specific emission factors play important roles in understanding source contributions and reducing the uncertainty in bottom-up inventories. Data used in this article are available at https://doi.org/10.6084/m9.figshare.12720989 (Lin et al., 2021).

Establishment and evaluation of anthropogenic black and organic carbon emissions over Central Plain, China

The Central China represented by Henan Province is one of the most haze-polluted regions in China with high levels of black carbon (BC) and organic carbon (OC) emissions. In this study, a 2016-based BC and OC emission inventory of Henan Province was developed based on detailed activity data and latest emission factors from different sources involving particulate matter. The total annual emissions of BC and OC were estimated to be 117.6 kt and 162.8 kt, respectively. Industrial process (44.2%) and industrial combustion (17.0%) were the top two contributors for BC emission, whereas OC emission was mainly from biomass burning (36.8%). Typical industrial cities were the major contributors to BC emission, by contrast, relatively higher OC emission was concentrated on agricultural cities in the southeast of Henan Province. The OC/BC ratios of industrial cities were generally lower than those of southeastern agricultural cities. The monthly variations presented that the highest BC and OC emissions were concentrated in January and June, respectively. Overall, in this study, the quantitative uncertainty ranges on the 95% confidence interval were approximately −30%–47% for BC and −38%–65% for OC. The emission inventory was also verified by qualitative comparison with other studies and indirect evaluation of OCpri/EC ratios, and the result was within a reasonable range. The ways of discussion and evaluation in this study can provide reference for other regions.

Temporal variability largely explains top-down/bottom-up difference in methane emission estimates from a natural gas production region

This study spatially and temporally aligns top-down and bottom-up methane emission estimates for a natural gas production basin, using multiscale emission measurements and detailed activity data reporting. We show that episodic venting from manual liquid unloadings, which occur at a small fraction of natural gas well pads, drives a factor-of-two temporal variation in the basin-scale emission rate of a US dry shale gas play. The midafternoon peak emission rate aligns with the sampling time of all regional aircraft emission studies, which target well-mixed boundary layer conditions present in the afternoon. A mechanistic understanding of emission estimates derived from various methods is critical for unbiased emission verification and effective greenhouse gas emission mitigation. Our results demonstrate that direct comparison of emission estimates from methods covering widely different timescales can be misleading.

EXPLORATION ON QUANTIFYING CARBON DIOXIDE (CO2) EMISSION FROM ROAD TRAFFIC IN MEGACITY

Abstract. As the increasing concern for climate change, quantification on greenhouse gas (GHG) emissions in urban scale has been a key component for local climate actions. To explore an approach on estimating the GHG emissions closely to the real-world condition, in this paper, we make efforts on counting the carbon dioxide (CO2) emissions from road traffic in Tokyo. The road traffic emissions mapping is achieved by linking spatial road line data with detailed activity data (traffic census). Through the data processing by Geographic Information System technique, the emissions of each road segment are estimated basing on the daily average traffic amount and speed of vehicles on each road segment. As our estimation, the CO2 emissions from road traffic of Tokyo in 2015 are about 16,323 Gg. The highlight points mainly refer to linking the traffic census information for the observed road segments on map and allocation efforts for unserved ones. As the limited amount of observation points in traffic census, accurate estimation for unobserved road segments is a challenge. Our approach overcomes it by assumption of a familiar traffic condition for all road segments in the same sub-city area. This approach could simulate the traffic patterns closely to the real traffic condition so that it will more effectively support the emission mitigation policies on road traffic for local climate.

Emissions from residential combustion sector: how to build a high spatially resolved inventory

Usually, annual emission data from residential combustion sector are spatially disaggregated by population density to the sub-municipality spatial level. The aim of the present work is to define a methodological approach to develop/build a high-resolution emission inventory from residential combustion following a bottom–up approach. The conceptual model considers different approaches by fuel category (solid fuel–wood, gas and liquid fuels) according to distinct spatial coverage and type of activity data available for each category. For solid fuels, detailed activity data (per district), disaggregated according to the number and type of equipment, burned wood species and consumption rate, as well as specific emission factors (per wood species) were used. With regard to the gas and liquid fuels, the total national emission by fuel type using national consumption data and broader emission factors was disaggregated to the sub-municipality scale based only on the number of heating equipment. The choice of these disaggregation factors was influenced by both data availability and relevance. The results of the new disaggregated emission data have been compared with emission values resulting from the classical top–down approach using census/population data. The selected case study is Portugal. The results pointed out that major differences exist when comparing both approaches, namely regarding the spatial distribution/allocation of emissions. In the new approach, emissions are more redistributed over the territory, while in the old distribution, emissions are concentrated in the coastal urban areas (with hotspots in the main urban areas of Porto and Lisbon).

Linking Time-Use Data to Explore Health Outcomes: Choosing to Vaccinate Against Influenza.

To inform public health and medical decision makers concerning vaccination interventions, a methodology for merging and analyzing detailed activity data and health outcomes is presented. The objective is to investigate relationships between individual's activity choices and their decision to receive an influenza vaccination. Data from the Behavioral Risk Factor Surveillance System (BRFSS) are used to predict vaccination rates in the American Time Use Survey (ATUS) data between 2003 and 2013 by using combined socioeconomic and demographic characteristics. The correlations between the extensive (do or not do) and intensive (how much) decisions to perform activities and influenza vaccination are further explored. Significant positive and negative correlations were found between several activities and vaccination. For some activities, the sign of the correlation flips when considering either the intensive or the extensive decision. This flip occurs with highly studied activities, like smoking. Correlations between activities and vaccination can provide an additional metric for targeting those least likely to vaccinate. The methodology outlined in this paper can be replicated to explore correlation among actions and other health outcomes.

An analysis of intrinsic and extrinsic factors affecting the activity of a nocturnal species: The wild boar

Over the last century, the wild boar (Sus scrofa) has become an important wildlife species in both economic and ecological terms. Considered a pest by some and a resource by others, its rapid increase in population and distribution has raised management concerns. Studies on activity rhythms may provide useful insights into its overall ecology and help develop effective management strategies. By examining highly detailed activity data collected by means of accelerometers fitted on GPS-collars, we studied wild boar daily activity rhythms and the effect of environmental conditions on their diurnal and nocturnal activity. We thus provided evidence of the predominantly nocturnal and monophasic activity of wild boars. All year round, we reported low activity levels during the day, which opportunistically increased under the most favourable environmental conditions. Activity was found to be significantly affected by such weather conditions as temperature, precipitation and air relative humidity. Moreover, we found that nocturnal activity slightly increased as moonlight increased. Part of our analysis was focused on the hunting period in order to investigate whether wild boars modify their activity levels in response to hunting disturbance. Our results suggested that wild boar nocturnal habits are not directly influenced by the current hunting disturbance, though we hypothesised that they may have evolved over several decades of hunting harassment. Alternatively, but not exclusively, nocturnal habits may have evolved as a low-cost strategy to achieve an optimum thermal balance (i.e., behavioural thermoregulation).

Current Status and Atmospheric Mercury Emissions Associated with Large-Scale Gold Smelting Industry in China

ABSTRACTMercury, being bound with metal concentrate, is released to atmosphere in the smelting process, and has adverse effects on surrounding ambient air quality and public health. In this study, based on literature and field investigations, onsite measurements of mercury emissions to the atmosphere for four large-scale gold smelters were estimated. Almost all of the atmospheric mercury released from the roasting pretreatment process occurred before acid plants. All the concentrations of mercury emitted from the four tested gold smelters were much lower than the emission standard of air pollutants for GB 9078–1996 Grade II level areas of 1000 µg m–3. The speciation of Hgp took up the largest proportions for the total mercury concentrations. Because of using high mercury oxidation efficiency of double-conversion double absorption (DCDA) technology, the proportions of Hg2+ in the exhaust flue gas was higher than Hg0. Average emission factors of atmospheric mercury for these four tested gold smelters were calculated to be 1.7 kg of Hg per ton gold produced, and the national total amount of mercury emitted from large-scale gold smelting industry in the year of 2014 was estimated to 639.9 kg. However, mercury emission factors obtained from present study were much lower than that in previous research. Thus, with the exact emission factor obtaining from more specific field-test data and the detailed activity data information about gold smelting industry, an accurate inventory of mercury emissions to the atmosphere from large-scale gold smelters in China can be updated in the future.

Increasing the use of large-scale public open spaces: A case study of the North Central Axis Square in Shenzhen, China

Urban public open space is a main component of urban space and an important carrier of public life. To explore the factors that influence the use of these spaces and to identify an effective technique for improving these areas, this paper focuses on the absence of spatial communication between large-scale public space users and characteristics of these spaces. A spatial database containing precise and detailed activity data on 13,468 public space users was created by systematically investigating the behavior of users in the North Central Axis Square in Shenzhen. Using ArcGIS, common patterns of user behavior were analyzed through a spatial analysis technique. The spatial characteristics of open space, including the provision and distribution of enough facilities, the meticulous division of space, the various and colorful edges, the moderate coverage ratio of vegetation, and the presence of shadow, are important factors that influence user behavior in large-scale public open spaces. To improve the use of these space, (1) various facilities must be established in public spaces, particularly regular and auxiliary seats with a recommended total length of 40 m per 1000 m2, to meet different demands; (2) the whole space must be divided into sub-areas with proper sizes (the recommended size is 1500 m2 to 2000 m2) and colorful interior edges; and (3) necessary shading facilities, such as roofs or tree sheds, must be provided depending on the climate in the area. A moderate vegetation coverage ratio (the recommended ration is 40%–50%) in every sub-area is also necessary.

Effects of improved spatial and temporal modeling of on-road vehicle emissions

Numerous emission and air quality modeling studies have suggested the need to accurately characterize the spatial and temporal variations in on-road vehicle emissions. The purpose of this study was to quantify the impact that using detailed traffic activity data has on emission estimates used to model air quality impacts. The on-road vehicle emissions are estimated by multiplying the vehicle miles traveled (VMT) by the fleet-average emission factors determined by road link and hour of day. Changes in the fraction of VMT from heavy-duty diesel vehicles (HDDVs) can have a significant impact on estimated fleet-average emissions because the emission factors for HDDV nitrogen oxides (NOx) and particulate matter (PM) are much higher than those for light-duty gas vehicles (LDGVs). Through detailed road link-level on-road vehicle emission modeling, this work investigated two scenarios for better characterizing mobile source emissions: (1) improved spatial and temporal variation of vehicle type fractions, and (2) use of Motor Vehicle Emission Simulator (MOVES2010) instead of MOBILE6 exhaust emission factors. Emissions were estimated for the Detroit and Atlanta metropolitan areas for summer and winter episodes. The VMT mix scenario demonstrated the importance of better characterizing HDDV activity by time of day, day of week, and road type. More HDDV activity occurs on restricted access road types on weekdays and at nonpeak times, compared to light-duty vehicles, resulting in 5–15% higher NOx and PM emission rates during the weekdays and 15–40% lower rates on weekend days. Use of MOVES2010 exhaust emission factors resulted in increases of more than 50% in NOx and PM for both HDDVs and LDGVs, relative to MOBILE6. Because LDGV PM emissions have been shown to increase with lower temperatures, the most dramatic increase from MOBILE6 to MOVES2010 emission rates occurred for PM2.5 from LDGVs that increased 500% during colder wintertime conditions found in Detroit, the northernmost city modeled. Implications: Air quality model performance relies partly on on-road mobile source emission inventories accurately allocated, both spatially and temporally. This work demonstrates the importance of characterizing the mix of heavy-duty diesel versus light-duty gasoline vehicle activity on an hourly basis on weekdays and weekends by road type. Incorporating detailed activity data increases weekday average NOx and PM emissions 5–15%, with early morning hour emission increases approaching 100%, compared to using one average vehicle activity mix. Application of the methodologies described in this paper will improve the accuracy of on-road emission inventories in the understanding of ozone photochemistry and PM formation.

Emissions inventory of anthropogenic PM2.5 and PM10 in Delhi during Commonwealth Games 2010

As part of the System of Air quality Forecasting and Research (SAFAR) project developed for air quality forecasting during the Commonwealth Games (CWG) – 2010, a high resolution Emission Inventory (EI) of PM 10 and PM 2.5 has been developed for the metropolitan city Delhi for the year 2010. The comprehensive inventory involves detailed activity data and developed for a domain of 70 km × 65 km with a 1.67 km × 1.67 km resolution covering Delhi and surrounding region using Geographical Information System (GIS) technique. The major sectors considered are, transport, thermal power plants , industries, residential and commercial cooking along with windblown road dust which is found to play a major role for Delhi environment. It has been found that total emissions of PM 10 and PM 2.5 including wind blown dust over the study area are found to be 236 Gg yr −1 and 94 Gg yr −1 respectively. The contribution of windblown road dust is found to be as high as 131 Gg yr −1 for PM 10 . ► A high resolution inventory of PM 10 and PM 2.5 are developed for mega city Delhi. ► Inventories are used for air quality forecasting during Commonwealth Games 2010. ► Inventory incorporate the activity data generated during one year field campaign. ► Total emission of PM 10 and PM 2.5 is found to be 236 Gg yr −1 and 94 Gg yr −1 for year 2010. ► Surprisingly emission from paved and unpaved roads is as high as 131 Gg yr −1 for PM 10 .

Quantifying the Air Quality Cobenefits of the Clean Development Mechanism in China

The Clean Development Mechanism's (CDM) contribution to sustainable development in host countries has come under criticism in recent years. Yet, there are no detailed country-wide estimates of air quality cobenefits from CDM projects. In this paper, we estimate the SO(2), PM(2.5), and NO(x) cobenefit rates (tonnes per kt CO(2)eq reduced) of 11 CDM project types for seven regions in China, using detailed activity data from Project Design Documents and emissions factors calculated from the GAINS-Asia model database. We forecast the CO(2)eq reductions by CDM projects to 2012 and their associated contributions to air pollution reductions, and avoided health and agricultural impacts. We expect CDM projects to yield notable SO(2) reductions in the coming years (1094 kt in 2010), with more modest reductions of PM(2.5) and NO(x) (79 and 270 kt in 2010, respectively). This suggests that the CDM could be making a nontrivial contribution to China's SO(2) reductions under the 11th Five-Year Plan. The monetized health and agricultural benefits from reduced PM and NO(x) amount to roughly 12 billion RMB per year in 2010, roughly one-third of the market value of the associated Certified Emission Reductions.

Review of Regional Locomotive Emission Modeling and the Constraints Posed by Activity Data

Diesel–electric locomotives used by U.S. freight railroads are relatively low emitters of criteria air pollutants and greenhouse gases when compared with competing modes. However, the continuous growth in goods movement is cause for concern because locomotive emissions may grow. Railroads account for only a small fraction of all mobile source emissions, but the concentration of emissions along rail facilities raises questions about equity, in particular, environmental justice, and the relative benefits of competing modes of goods movement. This paper provides a synthesis and review of current data and methods used to account for regional locomotive activity. Understanding data limitations and methodological issues at the regional scale provides a starting point for development of more spatially detailed locomotive emission models. Methods developed by the U.S. Environmental Protection Agency and the California Air Resources Board are considered. It is found that each method produces different results and is inadequate for use at the regional (or smaller) spatial scale. Problems arise from activity measures that ignore differences in geography and freight rail services between regions or that depend on detailed operational data that are no longer available. Although detailed activity data do exist, they are not always available because they are owned by private railroads. New methods should minimize the use of detailed or confidential railroad data yet still be sensitive to local factors. Fuel-based methods provide the most hope, but greater cooperation between regulatory agencies and railroads is required.

Substantiation of a lower estimate for the bunker inventory: Comment on “Updated emissions from ocean shipping” by James J. Corbett and Horst W. Koehler

Substantiation of a lower estimate for the bunker inventory: Comment on “Updated emissions from ocean shipping” by James J. Corbett and Horst W. Koehler