Air Pollutant Emission Inventory Research Articles

Modeling urban background air pollution in Quito, Ecuador

This study estimates air pollution at urban background level for Quito, Ecuador, using the Urban Background Model (UBM) developed at Aarhus University, Denmark. Hourly concentrations of CO, NO2, NOx, O3, PM2.5 and SO2 were calculated for the year 2009. UBM performance is evaluated at six monitoring locations. The air pollution emission inventory was scaled, using calibration factors, until modeled concentrations were in line with observations. Predicted values were graphically and statistically evaluated by comparison to measurements. The statistical assessment is conducted for: Fraction of predictions within a factor of two of the observations (FAC2), Fractional mean bias (FB), Normalized mean-square error (NMSE) and Normalized absolute difference (NAD). Results show that the UBM model successfully predicts concentrations of CO, NO2, NOx, O3 and PM2.5 while the predicted SO2 concentrations are unsatisfactory. PM2.5 modeling meets the criteria of acceptance, but their results depend largely on the regional levels, so the quality of this information is extremely relevant. The UBM model was applied for the years 2008 and 2010 using meteorological data retrieved from the modeling sites with emissions and calibration factors derived for the year 2009, showing a performance similar to that of 2009. The findings confirm the applicability of UBM to predict air pollution at the urban background level in Quito. Satisfactory results are obtained by applying meteorological data derived from any of the available monitoring stations. The unsatisfactory results for SO2 suggest that emission data should be reviewed and that this cannot be obtained simply by scaling.

Big geospatial data analysis for Canada’s Air Pollutant Emissions Inventory (APEI): using google earth engine to estimate particulate matter from exposed mine disturbance areas

ABSTRACTParticulate Matter (PM) emissions originating from mine waste and mine tailings can be hazardous to human health, depending on the ore type and processes used to extract ore. Until now, only a single, simple estimate of the total area of mine waste area across all of Canada has been available for calculating air quality emissions from this source. This single estimate, based on manual satellite interpretation completed in 1977, was extrapolated to estimate mine areas for all years from 1990 to the present. These area estimates were used annually to calculate the particulate matter from mines for the Canadian Air Pollutant Emissions Inventory (APEI); however, there is high uncertainty in these measurements of mine area and therefore in emissions estimates. In order to increase certainty in emissions estimates, the exposed mine waste areas must be mapped for each year. Mapping mine waste over areas as large as the Canadian landmass requires enormous quantities of data and considerable computational power, which will be compounded when a time-series analysis is required. Therefore, in this study, we have employed Google Earth Engine (GEE) Javascript API to map exposed mine areas in four “benchmark” years (1990, 2000, 2010, and current year 2018) as part of the APEI. A random forest classifier was trained using two separate datasets (Landsat-5 year 2000; and a combination of Landsat-8, Sentinel-1, and Sentinel-2 for the year 2018). Transfer learning was then used to apply the year 2000 model to the year 1990 and 2010 Landsat-5 imagery, which produced classification results for the four “benchmark” years in our time series. This tool has enabled the monitoring of mine growth over a 30-year period and has confirmed that overall the area of mines is growing in Canada. Overall, Google Earth Engine proved to be an invaluable tool in mapping exposed mine waste areas and would be similarly useful for any organization with large-area monitoring mandates or those interested in time-series analysis of the Landsat archive.

New Emission Factors for Calculation of Ammonia Volatilization From European Livestock Manure Management Systems

The largest source of ammonia (NH3) emissions to the atmosphere is NH3 from agriculture, the majority of which arise from livestock manure. The NH3 emitted is a threat to human health through the formation of fine particles, causes eutrophication of natural ecosystems and is a loss of fertiliser nitrogen (N). The Convention on Long-Range Transboundary Air Pollution (CLRTP) and the European Union National Emission Ceilings Directive (NECD) sets limits for national NH3 emissions and require the reporting of annual emission inventories to demonstrate compliance. The EMEP/EEA Air Pollutant Emission Inventory Guidebook provides emission factors (EFs) to support inventory compilation. Here we report the development of revised NH3 EFs for livestock housing, manure storage, field-applied manure and excreta deposited during grazing. Data from 276 studies were used with more than 70% of these data from peer reviewed journals, the remaining being from conference proceedings and scientific reports. For most sources, the new EFs are the weighted means of the emissions reported. The empirical ALFAM model was used to develop EFs for field-applied slurry. The standard deviation of the EFs were substantial, due to the breadth of the categories of livestock and management systems and because of variations in manure management and climate. The data collected will be available at http://www.alfam.dk

A GIS Based Emission Inventory of Air Pollutants from Mobile Sources in Morning Rush Hours; Case Study: Karaj

A GIS Based Emission Inventory of Air Pollutants from Mobile Sources in Morning Rush Hours; Case Study: Karaj

Analysis of the National Air Pollutant Emission Inventory (CAPSS 2015) and the Major Cause of Change in Republic of Korea

In 2015, air pollutant emissions in the Republic of Korea were 792,776 metric tons of CO, 1,157,728 metric tons of NOx, 352,292 metric tons of SOx, 604,243 metric tons of TSP, 233,177 metric tons of PM10, 98,806 tons of PM2.5, 15,934 metric tons of BC, 1,010,771 metric tons of VOCs, and 297,167 metric tons of NH3. Among major emission source categories, the main emission sources and the contributions to emissions, by pollutant, were as follows: road transport (31.0%), biomass burning (29.3%), and non-road transport (17.1%) for CO; road transport (31.9%), non-road transport (26.3%), and manufacturing industry (14.6%) for NOx; industrial processes (29.9%), energy production (25.9%), and manufacturing industry (24.2%) for SOx; fugitive dust (67.6%) manufacturing industry (20.1%) for TSP; fugitive dust (47.0%) and manufacturing industry (30.4%) for PM10; manufacturing industry (36.8%), fugitive dust (17.5%), and non-road transport (14.3%) for PM2.5; road transport (42.0%) and non-road transport(39.6%) for BC; solvent use (54.9%) and industrial processes (18.1%) for VOCs; and agriculture (77.8%) and industrial processes (13.3%) for NH3. The data we calculate can be used as official national emissions data for the establishment, implementation, and assessment of air quality-related policy, such as measures to deal with particulate matter, as well as for related modeling and other research.

Emission inventory of key sources of air pollution in Lebanon

Exposure to air pollutants has been associated with deleterious health effects that cause premature mortality and a range of morbidities. Air quality in the Mediterranean is of particular interest due to an array of environmental and anthropogenic conditions that make it an air-pollution hotspot. However, the scarcity of data for the region's emission inventories inhibits accurate and holistic assessment. Lebanon, located on the eastern board of the Mediterranean, faces several challenges including an unsustainable transport sector, an unregulated power generation sector, and high urban densities, all of which amplify the air-quality crisis. This paper presents an air pollutant emission inventory for two major emission sources in Lebanon, diesel generators and light duty vehicles (LDVs) of the transport sector, and uncovers trends for over a decade. The exhaust emissions for carbon dioxide, nitrogen oxide, carbon monoxide, sulfur dioxide, and fine particulate matter for diesel generators and for LDVs were estimated by assimilating different approaches and data sources through the use of survey data and national statistics for a higher tier. Our results uncovered that diesel generators consumed almost 1.6 million tons of fuel and emitted about 2 Gg of fine particulate matter in 2016. LDVs doubled in number over a decade and were responsible for approximately 0.20 Gg of fine particulate matter emissions in 2015. While the market for diesel generators appeared to have saturated, ownership of passenger cars per passengers continued to increase, while vehicle age, conditions, and, thus, emissions continued to augment. The results highlight the need for greater government intervention to meet the national electricity demand and promote public transportation and discourage private transportation, especially for energy-inefficient vehicles.

Discussion “City of Bitlis 2014 Air Pollution Emission Inventory” comments (2015; 5(2): 75-80)

This short not was written for the discussion of the manuscript titled as City of Bitlis 2014 Air Pollution Emission Inventory. Because it has several serious concerns and all of them discussed in the text.

A high-resolution inventory of air pollutant emissions from crop residue burning in China

Crop residue burning is an important source of air pollutants and strongly affects the regional air quality and global climate change. This study presents a detailed emission inventory of major air pollutants from crop residue burning for the year of 2014 in China by the bottom-up method. Activity data were investigated for 296 prefecture-level cities. Emission factors were determined for indoor and in-field burning separately. Regional differences were considered for the proportion of residue burned (PCRB), the ratio between indoor and in-field burning, and the ratio of straw to grain production. The emissions were estimated at prefecture-city level as the first step; then they were redistributed within a city based on 1-km resolution land-use, MODIS fire counts, and rural population. Temporal variation was determined according to farming practices in different regions and MODIS fire counts. Uncertainties were estimated using the Monte Carlo method. The total emissions from crop residue burning in China were estimated to be 0.13 (−47–92%) for BC, 0.71 (−48–92%) for OC, 1.77 (−48–91%) for PM2.5, 2.04 (−50–100%) for PM10, 0.16 (−59–133%) for SO2, 0.53 (−55–105%) for NOX, 0.12 (−47–93%) for NH3, 1.07 (−55–102%) for CH4, 1.85 (−43–74%) for NMVOC, 18.33 (−46–85%) for CO and 305.20 (−45–80%) for CO2, in unit of Tg yr−1. Our results are remarkably lower than those reported in previous studies, mainly because of the PCRB has decreased significantly in recent years. For most of the pollutants, indoor burning accounted for about 50–70% of the emissions. Rice, wheat and corn contributed more than 85% of the emissions, but their relative contributions varied a lot with region and season. High emissions were mostly located in the eastern China, central China and northeastern China, and temporally peaked in April, June and October, with different intensities in the north and the south. This study provides a useful basis for air quality modeling and the policy making of pollution control strategies.

A high spatial-temporal resolution emission inventory of multi-type air pollutants for Wuxi city

Currently, atmospheric emission inventory that includes multi-type pollutants and sources is still limited at the city level. This study developed a high-resolution emission inventory of eight pollutants based on detailed investigated data for Wuxi city in 2015. Results show that the total emissions of sulfur dioxide (SO2), nitrogen oxides (NOx), total suspended particulates (TSP), particulate matter with an aerodynamic diameter of 10 μm or less (PM10), particulate matter with an aerodynamic diameter of 2.5 μm or less (PM2.5), carbon monoxide (CO), ammonia (NH3), and volatile organic compounds (VOCs) were 36.9 kt, 95.2 kt, 287.3 kt, 127.8 kt, 46.0 kt, 626.4 kt, 10.9 kt, and 104.9 kt, respectively. Power plants were the major contributors of SO2 and NOx, accounting for 34.6% and 36.3% of the total emissions, respectively. Road dust was the dominant source of particulate matter, contributing 63.1%, 57.8% and 38.2% to the total TSP, PM10 and PM2.5 emissions, respectively. Around 67.0% of CO emissions were from iron and steel plants, while 50.1% of NH3 emissions were from livestock and poultry sectors. VOCs emissions largely came from oil refinery and chemical industry, with a share of 41.3%. High NH3 emissions were found in the southwest and northeast of Wuxi where high-intensity agriculture was located, while other pollutants were mainly concentrated on the eastern Wuxi where heavy industries, crowded traffic and highly dense population were located. Most pollutants from the industrial source exhibited the lowest value in February due to the Chinese Spring Festival with the exception of CO and NH3. This study helps to formulate the targeted air pollution control measures for Wuxi city by providing detailed information regarding the spatial-temporal distribution of multi-type air pollutants.

Recent development of a refined multiple air pollutant emission inventory of vehicles in the Central Plains of China

Central Plains region of China, represented by Henan Province, is facing serious air pollution problems. Vehicular exhaust emissions had adverse impacts on the atmospheric environment. The first comprehensive and novel vehicle emission inventory for Henan Province using vehicle kilometers traveled, localized emission factors, and activity data at city-level was developed. Furthermore, 3 km × 3 km gridded emission and temporal variations were determined by using localized information. Results show that the total emissions of sulfur dioxide (SO2), nitrogen oxides (NOx), carbon monoxide (CO), particular matter with aerodynamic diameter < 10 μm (PM10), aerodynamic diameter < 2.5 μm (PM2.5), volatile organic compounds (VOCs), VOCs-evaporation and ammonia in 2015 were 9.1, 533.4, 1190.7, 23.7, 21.6, 150.8, 31.5 and 10.4 Gg, respectively, and the emission intensities of the above pollutants were 0.05, 2.7, 6.0, 0.1, 0.1, 0.8, 0.2 and 0.05 g/km, respectively. Vehicles meeting the Primary China 1, China 3 and China 4 contributed 89.1%, 82.7%, 75.3%, 75.5%, 75.5%, 68.2%, 68.4% and 82.3% for SO2, NOx, CO, PM10, PM2.5, VOCs, VOCs-evaporation and ammonia emissions, respectively. Zhengzhou, Zhoukou, Nanyang, Luoyang, Shangqiu and Xinyang showed relatively higher emissions and contributed more than 50% of each pollutant. The spatial distribution indicated obvious characteristics of the road network, and high-level emission was concentrated in the downtown areas. Additionally, the ozone formation potential (OFP) based on the estimated speciated VOC emissions was 569.6 Gg in Henan Province. Aliphatic and aromatic hydrocarbons were the main species of VOCs, whereas olefins contributed the largest proportion of OFP, with 42.2%.

Contribution and uncertainty of sectorial and regional emissions to regional and global PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt; health impacts

Abstract. In this work we couple the HTAP_v2.2 global air pollutant emission inventory with the global source receptor model TM5-FASST to evaluate the relative contributions of the major anthropogenic emission sources (power generation, industry, ground transport, residential, agriculture and international shipping) to air quality and human health in 2010. We focus on particulate matter (PM) concentrations because of the relative importance of PM2.5 emissions in populated areas and the well-documented cumulative negative effects on human health. We estimate that in 2010, depending on the region, annual averaged anthropogenic PM2.5 concentrations varied between ca. 1 and 40 µg m−3, with the highest concentrations observed in China and India, and lower concentrations in Europe and North America. The relative contribution of anthropogenic emission sources to PM2.5 concentrations varies between the regions. European PM pollution is mainly influenced by the agricultural and residential sectors, while the major contributing sectors to PM pollution in Asia and the emerging economies are the power generation, industrial and residential sectors. We also evaluate the emission sectors and emission regions in which pollution reduction measures would lead to the largest improvement on the overall air quality. We show that air quality improvements would require regional policies, in addition to local- and urban-scale measures, due to the transboundary features of PM pollution. We investigate emission inventory uncertainties and their propagation to PM2.5 concentrations, in order to identify the most effective strategies to be implemented at sector and regional level to improve emission inventories, knowledge and air quality modelling. We show that the uncertainty of PM concentrations depends not only on the uncertainty of local emission inventories, but also on that of the surrounding regions. Countries with high emission uncertainties are often impacted by the uncertainty of pollution coming from surrounding regions, highlighting the need for effective efforts in improving emissions not only within a region but also from extra-regional sources. Finally, we propagate emission inventory uncertainty to PM concentrations and health impacts. We estimate 2.1 million premature deaths per year with an uncertainty of more than 1 million premature deaths per year due to the uncertainty associated only with the emissions.

Satellite-derived emissions of carbon monoxide, ammonia, and nitrogen dioxide from the 2016 Horse River wildfire in the Fort McMurray area

Abstract. In May 2016, the Horse River wildfire led to the evacuation of ∼ 88 000 people from Fort McMurray and surrounding areas and consumed ∼ 590 000 ha of land in Northern Alberta and Saskatchewan. Within the plume, satellite instruments measured elevated values of CO, NH3, and NO2. CO was measured by two Infrared Atmospheric Sounding Interferometers (IASI-A and IASI-B), NH3 by IASI-A, IASI-B, and the Cross-track Infrared Sounder (CrIS), and NO2 by the Ozone Monitoring Instrument (OMI). Daily emission rates were calculated from the satellite measurements using fire hotspot information from the Moderate Resolution Imaging Spectroradiometer (MODIS) and wind information from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis, combined with assumptions on lifetimes and the altitude range of the plume. Sensitivity tests were performed and it was found that uncertainties of emission estimates are more sensitive to the plume shape for CO and to the lifetime for NH3 and NOx. The satellite-derived emission rates were ∼ 50–300 kt d−1 for CO, ∼ 1–7 kt d−1 for NH3, and ∼ 0.5–2 kt d−1 for NOx (expressed as NO) during the most active fire periods. The daily satellite-derived emission estimates were found to correlate fairly well (R∼0.4–0.7) with daily output from the ECMWF Global Fire Assimilation System (GFAS) and the Environment and Climate Change Canada (ECCC) FireWork models, with agreement within a factor of 2 for most comparisons. Emission ratios of NH3∕CO, NOx∕CO, and NOx∕NH3 were calculated and compared against enhancement ratios of surface concentrations measured at permanent surface air monitoring stations and by the Alberta Environment and Parks Mobile Air Monitoring Laboratory (MAML). For NH3∕CO, the satellite emission ratios of ∼ 0.02 are within a factor of 2 of the model emission ratios and surface enhancement ratios. For NOx∕CO satellite-measured emission ratios of ∼0.01 are lower than the modelled emission ratios of 0.033 for GFAS and 0.014 for FireWork, but are larger than the surface enhancement ratios of ∼0.003, which may have been affected by the short lifetime of NOx. Total emissions from the Horse River fire for May 2016 were calculated and compared against total annual anthropogenic emissions for the province of Alberta in 2016 from the ECCC Air Pollutant Emissions Inventory (APEI). Satellite-measured emissions of CO are ∼1500 kt for the Horse River fire and exceed the total annual Alberta anthropogenic CO emissions of 992.6 kt for 2016. The satellite-measured emissions during the Horse River fire of ∼30 kt of NH3 and ∼7 kt of NOx (expressed as NO) are approximately 20 % and 1 % of the magnitude of total annual Alberta anthropogenic emissions, respectively.

Bitlis Air Pollution Emission Inventory and Estimation of Health Effects by Multiple Linear Regression

Bitlis ili topoğrafik yapısı ve coğrafik konumu nedeni ile karasal bir iklime sahiptir. Ülkemizde sıcaklık ortalaması düşük illerden biri olan Bitlis’te kışlar oldukça soğuk ve uzun geçmektedir. Uzun süren kış döneminin çok soğuk ve ağır geçmesi nedeniyle kömürün bilinçsizce ve aşırı tüketiminden dolayı en fazla hava kirliliği kış aylarında görülür. Kış aylarında kullanılan bu yakıtların neden olduğu kirletici konsantrasyonları hava kalitesi sınır değerlerini aşmamakla birlikte inversiyon etkisiyle Bitlis ili hava kalitesini olumsuz yönde etkilemektedir. Hava kirliliğine neden olan bu gaz veya toz halindeki kirleticiler solunum sistemine zarar vermektedir. Hava kirliliğinin insan sağlığına olan etkileri maruziyet süresine ve kirletici miktarına göre değişmektedir. Son yıllarda hava kirliliğinin kısa süreli ve uzun süreli etkileriyle ilgili birçok çalışma yapılmış ve dolaşım sistemine bağlı olarak hastalık ve ölüm oranlarının arttığı gözlemlenmiştir. Bitlis Çevre ve Şehircilik İl Müdürlüğü’nden alınan verilere göre 2015 yılında, Bitlis iline giriş yapan kömür miktarı toplam 55.118,63 kg.’dır. 2015 yılında, hava kirliliğindeki birincil kirleticilerden SOx, NOx, PM10 ve CO parametreleri sırasıyla, 1.980 kg, 176 kg, 210 kg ve 2.211 kg atmosfere deşarj edildiği hesaplanmıştır. Bu çalışmada, kirletici parametrelerin SOx, NOx, CO ve PM10 ölçüm değerlerinden yararlanılmıştır. Bitlis Sağlık İl Müdürlüğü’nden solunum ve akciğer hastalıklarından kaynaklı hastanelere başvuran hasta sayısı verileri alınmıştır. Bu verilere göre çoklu lineer regresyon yöntemi kullanılarak gelecek yıldaki hasta sayısı tahmin edilmiştir. Gerçek veriler ve tahmin verileri karşılaştırılarak R2=0,98 oranında uyum sağlanmıştır.

Emission inventory of anthropogenic air pollutant sources and characteristics of VOCs species in Sichuan Province, China

The purpose of this paper is to develop an emission inventory of anthropogenic air pollutants and VOCs species in Sichuan Province. Based on the anthropogenic source activity data collected in different cities of Sichuan Province and the selected emission factors, the 1 km × 1 km gridded atmospheric air pollutant emission inventory of 2015 was developed in the “bottom-up” and “top-down” approaches with the GIS technology. The results showed that the emissions of SO2, NOX, CO, PM10, PM2.5, BC, OC, VOCs and NH3 from anthropogenic sources in Sichuan Province were 444.9 kt, 820.0 kt, 3773.1 kt, 1371.6 kt, 537.5 kt, 28.7 kt, 53.1 kt, 923.6 kt and 988.0 kt, respectively. Power plants and other industrial combustion boilers contributed more than 95% of SO2 emission. Transportation, fossil fuel burning and industrial process contributed 54%, 23% and 20% of NOx emission respectively. Industrial process dominated by steel production and building material manufacturing contributed 20% of PM10 emission and 34% of PM2.5 emission. Fugitive dust dominated by road fugitive dust contributed 60% of PM10 emission and 35% of PM2.5 emission respectively. Biomass burning contributed 33% of BC emission and 51% of OC emission respectively. Solvent use of mechanical processing, building decoration, electronic equipment manufacturing, printing and furniture industry contributed 46% of VOCs emission. NH3 mainly came from the emission of agricultural sectors, such as livestock breeding and N-fertilizer application, which contributed 70% and 25% of NH3 emission respectively. The percentage of alkanes, alkenes, alkynes, aromatics, OVOCs, halohydrocarbons and other VOCs in the total VOCs emission were 17%, 9%, 2%, 23%, 22%, 4% and 23%, respectively. Ethene, m-xylene, toluene, propene, formaldehyde, o-xylene, 1, 2, 4-trimethyl benzene, 1-butene, p-xylene and ethyl benzene were the most critical chemical species for the formation of ozone pollution in Sichuan Province contributing 50% of the total OFP. Various air pollutants and OFP were mainly distributed in places with the densest population and well-developed agriculture and industry in Sichuan Basin and some areas of Panzhihua. The Chengdu Plain urban agglomerations, represented by Chengdu, Deyang and Mianyang, were the main areas with concentrated pollutant emissions in Sichuan Basin.

Spatial and temporal dynamics of air-pollutant emission inventory of steel industry in China: A bottom-up approach

As one of the energy-intensive and high-polluted industries, steel industry is facing great challenges of energy-saving and emission reduction. In this study, a bottom-up approach is used to build the inventory of air pollutants in China's steel industry. We analyzed the temporal and spatial characteristics of the atmospheric emission inventory of China's steel industry. The results show that: (1) In 2015, the total amount of SO2, NOx, PM, PM2.5, PM10 and PCDD/Fs emissions from Chinese steel industry were 1697.58kt, 1017.24kt, 4028.01kt, 849.02kt, 1330.53kt and 1216.83g I-TEQ, respectively. SO2, NOx, PM, PM10 and PCDD/Fs emissions mainly came from the sintering process, and PM2.5 came from the converter process. (2) NOx emissions show an “upward” trend, while SO2 emissions depict a “slowdown” trend. PM, PM2.5, PM10 and PCDD/Fs emissions present inverse U-shape trends. (3) The air pollutant inventory of Chinese steel industry indicates obviously spatial characteristics. Taking SO2 emissions in 2015 as an example, SO2 emissions from steel industry in North China and East China reached 1006.63kt, accounting for 65.2% of the total emissions. Hebei, Jiangsu and Shandong provinces where locate intensive traditional steel enterprises with majority of steel products generated SO2 emissions of approximately 404.04kt, 171.83kt and 156.28kt respectively, accounting for 23.80%, 10.12% and 9.20% total emissions of China's steel industry. (4) The simulation model of air pollutants (SO2, PM) was established taking adjusted production capacity and layout of steel industry into consideration, the simulation suggests that SO2 and PM emissions are projected to decrease by 1Mt, 2 Mt, respectively.

Emission Inventory and Characteristics of Anthropogenic Air Pollutant Sources in the Sichuan Province

Based on anthropogenic source activity data and emission factors for the Sichuan Province, the 1 km×1 km-gridded atmospheric air pollutant emission inventory of 2015 was developed in combination with GIS technology and the combined "bottom-up" and "top-down" construction method. The results show that the total emission of SO2, NOx, CO, PM10, PM2.5, BC, OC, VOCs, and NH3 in Chengdu is 444.9×103, 820.0×103, 3773.1×103, 1371.6×103, 537.5×103, 28.7×103, 53.1×103, 923.6×103, and 988.0×103 t, respectively. Power plants and other industrial combustion boilers contribute more than 95% of the SO2 emissions. Mobile, fossil fuel combustion, and industrial process sources contribute 54%, 23%, and 20% of the NOx emissions, respectively. The industrial process of steel production and building materials manufacturing contribute 20% PM10 of the emissions and take up 34% PM2.5 of the emissions. Fugitive dust and road fugitive dust contributes 60% PM10 and 35% PM2.5 of the emissions, respectively. Biomass combustion contributes 33% BC and 51% OC of the emissions, respectively. The solvent use of mechanical processing, building decoration, electronic equipment manufacturing, and printing and furniture industry contribute 46% of the VOCs of the emissions. The NH3 emissions mainly orginate from the sources of livestock feeding and nitrogen fertilizers, accounting for 70% and 25% of the NH3 emissions, respectively. The spatial distribution of the emissions shows that high emissions are mainly distributed in the most densely populated, agricultural, and industrial more developed areas in Panzhihua and the Sichuan Basin. The urban agglomerations of the Chengdu Plain, represented by Chengdu, Deyang, and Mianyang, are the areas with emission concentration in the Sichuan Basin. The emissions inventory in this study has uncertainties. More fundamental studies on activity data should be conducted and the emission factors of typical emission sources should be further localized to improve the emission inventory and prevention and control of complex air pollution in the Sichuan Province and provide scientific support.

Emission characteristics and high-resolution spatial and temporal distribution of pollutants from motor vehicles in Chengdu, China

The establishment of the air pollutant emission inventory of urban motor vehicles is the key to control motor vehicle pollution; therefore, the purpose of this paper is to develop an air pollutant emission inventory of urban motor vehicles in Chengdu. The IVE model localization including the actual measurement correction was completed, the motor vehicle emission factors on various types of roads in Chengdu's different development areas were calculated, and the 1 km × 1 km on-road mobile source emission inventory for the year of 2016 was established in the bottom-up method based on the collected data on road network, traffic flow, vehicle driving cycle, the vehicle population in Chengdu, etc. The inventory showed that the CO, VOCs, NOx, SO2, PM10 and NH3 emissions of Chengdu's on-road mobile sources had reached 432.2 kt, 44.8 kt, 72.4 kt, 0.4 kt, 11.3 kt and 6.2 kt respectively in 2016. Light-duty vehicle, medium-duty vehicle, and heavy-duty vehicle were the major contributors to CO emissions, and their CO emissions accounted for 44%, 14% and 11% of the total CO emission respectively; VOCs mainly came from light-duty vehicle, motorcycle, and heavy-duty truck, the VOCs emissions of which accounted for 49%, 19% and 10% of the total VOCs emission respectively; NOx and SO2 mainly came from light-duty vehicle and heavy-duty truck, the NOx emissions of which accounted for 25% and 48% of the total NOx emission, and the SO2 emissions of which accounted for 70% and 14% of the total SO2 emission respectively; in addition, the heavy-duty truck caused PM10 emission accounting for 78% of the total PM10 emission, while the light-duty vehicle caused NH3 emission accounting for 81% of the total NH3 emission. The spatial distribution of on-road mobile source pollutant emissions showed that emissions were concentrated in the downtown with a decreasing trend from the downtown to the suburbs and the outer suburbs. Based on the 95% confidence intervals, the uncertainties of CO, VOCs and NOx emission estimations were relatively low with relative errors ranging from - 68% to 87%, 58%–84% and 97%–75%. Considering the uncertainties of the traffic flows and emission factors, the on-road mobile source emission inventory shall be further improved to provide important scientific research supporting for the prevention and control of air pollution in Chengdu.

A methodology for the development of urban energy balances: Ten years of application to the city of Madrid

This paper presents a methodology for the development of urban energy balances, which has been applied to the city of Madrid for the last ten years and is consistent with the guidelines of the International Energy Agency. The methodology is structured into three major modules: energy imports into the municipality, energy generation from either internal or external sources, and final energy consumption.The paper shows results for the most recent year for which data are available, as well as historical series for the period 2006–2015. It also includes a comparison of the main energy indicators with the national situation.The experience acquired during the ten years performing the energy balance in Madrid served to improve both the greenhouse gases and the air pollutant emission inventories, keeping their consistent with the energy balance, as well as to diagnose the city's current energy situation. This assessment was used to define a series of policies, energy saving and diversification measures, collected in different municipal plans, with the aim of decreasing the external energy dependence and the energy consumption, thereby lowering greenhouse gases emissions. Maintaining the same methodology and updating the historical series each year also allowed tracking the city's energy policy, as well as the evolution of the main energy indicators (i.e. per capita consumption and energy intensity). The methodology could be replicated in other cities worldwide, yielding improvements in their urban emission inventories.

Emission Inventory and Prediction of Non-road Machineries in the Yangtze River Delta Region, China

An air pollutant emission inventory of non-road machineries for the Yangtze River Delta (YRD) region was developed, based on local surveys and relative indicator predictions for cities in the region. Population, fuel consumption, and air pollutant emissions of non-road machineries were predicted for the period 2005 to 2025. The population of non-road machineries in the YRD region in 2014 was 8.23×106 units, diesel consumption was about 9.95×106 t, and SO2, NOx, CO, VOCs, PM10, and PM2.5 emissions were 5.5×103, 4.9×105, 7.6×105, 1.1×105, 2.9×104, and 2.7×104 t, respectively. Agricultural machineries accounted for 93% of the total population, with their CO and VOC emissions contributing 88% and 77% of respective totals. Construction machineries contributed 49% and 35% of NOx and PM2.5 emissions. Air pollutant emissions from non-road machineries were mainly concentrated in the middle and northern cities of the YRD region. During the period 2005-2014, the growth rates of population, fuel consumption, and air pollutant emissions of non-road machineries in the YRD region were relatively high. It is estimated that growth will be slowing down in 2020 and 2025. Diesel consumption will increase by 2% and 8% in 2020 and 2025, respectively, compared with 2014 levels. By 2020, SO2, NOx, CO, VOCs, PM10, and PM2.5 emissions will decrease by 97%, 10%, 3%, 10%, 11%, and 11%, respectively; by 2025, these decreases will reach 97%, 16%, 3%, 15%, 21%, and 21%, respectively. It is expected that air pollutant emissions from non-road machineries will continue to decline in future. However, the decreasing trend of NOx, VOCs, and PM2.5 emissions from motor vehicles reached 22%, 50%, and 48%, much greater than that of non-road machinery. The emission contributions of non-road machinery will become increasingly significant in future. It is necessary to accelerate the scrappage of old machinery and to further promote emission standards for new machinery to reduce emissions from non-road machineries.

Atmospheric emission inventory of multiple pollutants from civil aviation in China: Temporal trend, spatial distribution characteristics and emission features analysis

A detailed comprehensive emission inventory of multiple air pollutants from civil aviation in China for the historical period of 1980–2015 is developed by using an approach of combining bottom-up with top-down for the first time. Annual emissions of various pollutants present a rapidly ascending trend along with the increase of economic volume and population, which are estimated at approximately 4.77 kt HC, 59.63 kt CO, 304.77 kt NOx, 59,961 kt CO2, 19.04 kt SO2, 3.32 kt PM2.5, 1.59 kt BC, 1.06 kt OC and 5.44 t heavy metals (HMs), respectively, by the year 2015. We estimate the local emissions in 208 domestic civil airports and allocate the total cruise emissions onto 299 main domestic flight segments with surrogate indexes, such as route distance, cargo and passenger turnover. The results demonstrate that emission intensities in central and eastern China are much higher than those in northeastern and western China, and these regions are characterized with high population density, huge economy volume, as well as transit convenience. Furthermore, we have explored emission characteristics of multiple pollutants under different operation modes in 2015. For PM2.5, SO2/CO2/HMs and NOx, the emissions from cruise process constitute the dominant contributor with a share of 89%, 92% and 81%, of the associated total emissions, respectively, comparing with 76% and 71% of the total CO and HC emissions release from Landing and Take-off (LTO) process. Consequently, there are notably different emission characteristics from different flight processes due to various combustion status of aviation fuel. In addition, we predict the future trends of multi-pollutants emissions from China's civil aviation industry through 2050 under three scenarios, and the results indicate that the reduction from the improvement of new technology or new national standards would be largely offset by the rise in multi-pollutants emissions from rapidly aviation fuel growth.