PM10 emission inventory in Ile de France for transport and industrial sources: PM10 re-suspension, a key factor for air quality

2000 ◽  
Vol 15 (6-7) ◽  
pp. 575-581 ◽  
Author(s):  
Anne Jaecker-Voirol ◽  
Philippe Pelt
Keyword(s):  
Air Quality ◽  
Emission Inventory ◽  
Key Factor ◽  
Industrial Sources ◽  
Pm10 Emission

scholarly journals Development of a new emission reallocation method for industrial sources in China

2021 ◽  
Vol 21 (17) ◽  
pp. 12895-12908
Author(s):  
Yun Fat Lam ◽  
Chi Chiu Cheung ◽  
Xuguo Zhang ◽  
Joshua S. Fu ◽  
Jimmy Chi Hung Fung
Keyword(s):  
Air Quality ◽  
Emission Inventory ◽  
Population Based ◽  
New Method ◽  
Industrial Emissions ◽  
Unified Framework ◽  
Bottom Up ◽  
Bay Area ◽  
Industrial Buildings ◽  
Industrial Sources

Abstract. An accurate emission inventory is a crucial part of air pollution management and is essential for air quality modelling. One source in an emission inventory, an industrial source, has been known with high uncertainty in both location and magnitude in China. In this study, a new reallocation method based on blue-roof industrial buildings was developed to replace the conventional method of using population density for the Chinese emission development. The new method utilized the zoom level 14 satellite imagery (i.e. Google®) and processed it based on hue, saturation, and value (HSV) colour classification to derive new spatial surrogates for province-level reallocation, providing more realistic spatial patterns of industrial PM2.5 and NO2 emissions in China. The WRF-CMAQ-based PATH-2016 model system was then applied with the new processed industrial emission input in the MIX inventory to simulate air quality in the Greater Bay Area (GBA) area (formerly called Pearl River Delta, PRD). In the study, significant root mean square error (RMSE) improvement was observed in both summer and winter scenarios in 2015 when compared with the population-based approach. The average RMSE reductions (i.e. 75 stations) of PM2.5 and NO2 were found to be 11 µg m−3 and 3 ppb, respectively. Although the new method for allocating industrial sources did not perform as well as the point- and area-based industrial emissions obtained from the local bottom-up dataset, it still showed a large improvement over the existing population-based method. In conclusion, this research demonstrates that the blue-roof industrial allocation method can effectively identify scattered industrial sources in China and is capable of downscaling the industrial emissions from regional to local levels (i.e. 27 to 3 km resolution), overcoming the technical hurdle of ∼ 10 km resolution from the top-down or bottom-up emission approach under the unified framework of emission calculation.

scholarly journals Bottom-Up Emission Inventory and Its Spatio-Temporal Distribution from Paved Road Dust Based on Field Investigation: A Case Study of Harbin, Northeast China

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 449
Author(s):  
Lili Li ◽  
Kun Wang ◽  
Zhijian Sun ◽  
Weiye Wang ◽  
Qingliang Zhao ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Emission Inventory ◽  
Road Dust ◽  
Field Investigation ◽  
Pollutant Emissions ◽  
County Level ◽  
Bottom Up ◽  
Spatial Allocation ◽  
Paved Road

Road dust is one of the primary sources of particulate matter which has implications for air quality, climate and health. With the aim of characterizing the emissions, in this study, a bottom-up approach of county level emission inventory from paved road dust based on field investigation was developed. An inventory of high-resolution paved road dust (PRD) emissions by monthly and spatial allocation at 1 km × 1 km resolution in Harbin in 2016 was compiled using accessible county level, seasonal data and local parameters based on field investigation to increase temporal-spatial resolution. The results demonstrated the total PRD emissions of TSP, PM10, and PM2.5 in Harbin were 270,207 t, 54,597 t, 14,059 t, respectively. The temporal variation trends of pollutant emissions from PRD was consistent with the characteristics of precipitation, with lower emissions in winter and summer, and higher emissions in spring and autumn. The spatial allocation of emissions has a strong association with Harbin’s road network, mainly concentrating in the central urban area compared to the surrounding counties. Through scenario analysis, positive control measures were essential and effective for PRD pollution. The inventory developed in this study reflected the level of fugitive dust on paved road in Harbin, and it could reduce particulate matter pollution with the development of mitigation strategies and could comply with air quality modelling requirements, especially in the frigid region of northeastern China.

scholarly journals TNO-MACC_II emission inventory; a multi-year (2003–2009) consistent high-resolution European emission inventory for air quality modelling

2014 ◽  
Vol 14 (20) ◽  
pp. 10963-10976 ◽  
Author(s):  
J. J. P. Kuenen ◽  
A. J. H. Visschedijk ◽  
M. Jozwicka ◽  
H. A. C. Denier van der Gon
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
High Resolution ◽  
Emission Inventory ◽  
Point Sources ◽  
Air Quality Modelling ◽  
Emission Data ◽  
Data Set ◽  
Spatially Distributed ◽  
Reported Data

Abstract. Emissions to air are reported by countries to EMEP. The emissions data are used for country compliance checking with EU emission ceilings and associated emission reductions. The emissions data are also necessary as input for air quality modelling. The quality of these "official" emissions varies across Europe. As alternative to these official emissions, a spatially explicit high-resolution emission inventory (7 × 7 km) for UNECE-Europe for all years between 2003 and 2009 for the main air pollutants was made. The primary goal was to supply air quality modellers with the input they need. The inventory was constructed by using the reported emission national totals by sector where the quality is sufficient. The reported data were analysed by sector in detail, and completed with alternative emission estimates as needed. This resulted in a complete emission inventory for all countries. For particulate matter, for each source emissions have been split in coarse and fine particulate matter, and further disaggregated to EC, OC, SO4, Na and other minerals using fractions based on the literature. Doing this at the most detailed sectoral level in the database implies that a consistent set was obtained across Europe. This allows better comparisons with observational data which can, through feedback, help to further identify uncertain sources and/or support emission inventory improvements for this highly uncertain pollutant. The resulting emission data set was spatially distributed consistently across all countries by using proxy parameters. Point sources were spatially distributed using the specific location of the point source. The spatial distribution for the point sources was made year-specific. The TNO-MACC_II is an update of the TNO-MACC emission data set. Major updates included the time extension towards 2009, use of the latest available reported data (including updates and corrections made until early 2012) and updates in distribution maps.

scholarly journals Ensemble Predictions of Air Pollutants in China in 2013 for Health Effects Studies Using WRF/CMAQ Modeling System with Four Emission Inventories

2017 ◽  
Author(s):  
Jianlin Hu ◽  
Xun Li ◽  
Lin Huang ◽  
Qi Ying ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Air Quality ◽  
Health Effects ◽  
Air Pollutants ◽  
Emission Inventory ◽  
Model Performance ◽  
Observation Data ◽  
Emission Inventories ◽  
Air Quality Model ◽  
Quality Model ◽  
Future Health

Abstract. Accurate exposure estimates are required for health effects analyses of severe air pollution in China. Chemical transport models (CTMs) are widely used tools to provide detailed information of spatial distribution, chemical composition, particle size fractions, and source origins of pollutants. The accuracy of CTMs' predictions in China is largely affected by the uncertainties of public available emission inventories. The Community Multi-scale Air Quality model (CMAQ) with meteorological inputs from the Weather Research and Forecasting model (WRF) were used in this study to simulate air quality in China in 2013. Four sets of simulations were conducted with four different anthropogenic emission inventories, including the Multi-resolution Emission Inventory for China (MEIC), the Emission Inventory for China by School of Environment at Tsinghua University (SOE), the Emissions Database for Global Atmospheric Research (EDGAR), and the Regional Emission inventory in Asia version 2 (REAS2). Model performance was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O3 and PM2.5 with the four inventories generally meet the criteria of model performance, but difference exists in different pollutants and different regions among the inventories. Ensemble predictions were calculated by linearly combining the results from different inventories under the constraint that sum of the squared errors between the ensemble results and the observations from all the cities was minimized. The ensemble annual concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB) and mean fractional errors (MFE) of the ensemble predicted annual PM2.5 at the 60 cities are −0.11 and 0.24, respectively, which are better than the MFB (−0.25–−0.16) and MFE (0.26–0.31) of individual simulations. The ensemble annual 1-hour peak O3 (O3-1 h) concentrations are also improved, with mean normalized bias (MNB) of 0.03 and mean normalized errors (MNE) of 0.14, compared to MNB of 0.06–0.19 and MNE of 0.16–0.22 of the individual predictions. The ensemble predictions agree better with observations with daily, monthly, and annual averaging times in all regions of China for both PM2.5 and O3-1 h. The study demonstrates that ensemble predictions by combining predictions from individual emission inventories can improve the accuracy of predicted temporal and spatial distributions of air pollutants. This study is the first ensemble model study in China using multiple emission inventories and the results are publicly available for future health effects studies.

scholarly journals Emission Inventory Processing of Biomass Burning from A Global Dataset for Air Quality Modeling

2020 ◽  
Author(s):  
Ernesto Pino-Cortés ◽  
Samuel Carrasco ◽  
Luis A. Díaz-Robles ◽  
Francisco Cubillos ◽  
Fidel Vallejo ◽  
...  
Keyword(s):  
Air Quality ◽  
Biomass Burning ◽  
Emission Inventory ◽  
Sparse Matrix ◽  
Local Level ◽  
Air Quality Modeling ◽  
Simulation Software ◽  
Atmospheric Pollutants ◽  
Fire Emissions ◽  
Quality Modeling

Wildfires generate large amounts of atmospheric pollutants yearly. The development of an emissions inventory for this activity is a challenge today, mainly to perform modeling of air quality. There are free available databases with historical information about this source. The main goal of this study was to process the results of biomass burning emissions for the year 2014 from the Global Fire Assimilation System (GFAS). The pollutants studied were the black carbon, the organic carbon, fine and coarse particulate matter, respectively. The inputs were pre-formatted to enter to the simulation software of the emission inventory. In this case, the Sparse Matrix Operator Kernel Emissions (SMOKE) was used and the values obtained in various cities were analyzed. As a result, the spatial distribution of the forest fire emissions in the Southern Hemisphere was achieved, with the polar stereographic projection. The highest emissions were located in the African continent, followed by the northern region of Australia. Future air quality modeling at a local level could apply the results and the methodology of this study. The biomass burning emissions could add a better performance of the results and more knowledge on the effect of this source.

Volatile organic compounds sources in Paris in spring 2007. Part II: source apportionment using positive matrix factorisation

2011 ◽  
Vol 8 (1) ◽  
pp. 91 ◽  
Author(s):  
Cécile Gaimoz ◽  
Stéphane Sauvage ◽  
Valérie Gros ◽  
Frank Herrmann ◽  
Jonathan Williams ◽  
...  
Keyword(s):  
Volatile Organic Compounds ◽  
Organic Compound ◽  
Organic Compounds ◽  
Emission Inventory ◽  
Positive Matrix ◽  
Air Mass ◽  
Volatile Organic ◽  
Industrial Sources ◽  
Fuel Evaporation ◽  
Matrix Factorisation

Environmental context Volatile organic compounds are key compounds in atmospheric chemistry as precursors of ozone and secondary organic aerosols. To determine their impact at a megacity scale, a first important step is to characterise their sources. We present an estimate of volatile organic compound sources in Paris based on a combination of measurements and model results. The data suggest that the current emission inventory strongly overestimates the volatile organic compounds emitted from solvent industries, and thus needs to be corrected. Abstract A positive matrix factorisation model has been used for the determination of volatile organic compound (VOC) source contributions in Paris during an intensive campaign (May–June 2007). The major sources were traffic-related emissions (vehicle exhaust, 22% of the total mixing ratio of the measured VOCs, and fuel evaporation, 17%), with the remaining emissions from remote industrial sources (35%), natural gas and background (13%), local sources (7%), biogenic and fuel evaporation (5%) and wood-burning (2%). It was noted that the remote industrial contribution was highly dependent on the air-mass origin. During the period of oceanic influences (when only local and regional pollution was observed), this source made a relatively low contribution (<15%), whereas the source contribution linked to traffic was high (54%). During the period of continental influences (when additional continental pollution was observed), remote industrial sources played a dominant role, contributing up to 50% of measured VOCs. Finally, the positive matrix factorisation results obtained during the oceanic air mass-influenced period were compared with the local emission inventory. This comparison suggests that the VOC emission from solvent industries might be overestimated in the inventory, consistent with findings in other European cities.

scholarly journals Assessment of co-benefits of black carbon emission reduction measures in Southeast Asia: Part 1 emission inventory and simulation for the base year 2007

2017 ◽  
Author(s):  
Didin Agustian Permadi ◽  
Nguyen Thi Kim Oanh ◽  
Robert Vautard
Keyword(s):  
Air Quality ◽  
Southeast Asia ◽  
Black Carbon ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Model Evaluation ◽  
Emission Reduction ◽  
Emission Inventory ◽  
Climate Forcing ◽  
Urban Site

Abstract. This research assessed the potential co-benefits associated with selected black carbon (BC) emission reduction measures on mitigation of air pollution and climate forcing in Southeast Asia (SEA). This paper presents Part 1 of the research with details on the emission inventory (EI) results and the WRF/CHIMERE model performance evaluation. The SEA regional emissions for 2007 were updated with our EI results for Indonesia, Thailand and Cambodia and used for the model input. WRF/CHIMERE simulated PM10, PM2.5 and BC over the SEA domain (0.25º x 0.25º) of the year 2007 and the results were evaluated against the available monitoring data in the domain. WRF hourly simulation results were evaluated using the observed data at 8 international airport stations in 5 SEA countries and showed a satisfactory performance. WRF/CHIMERE results for PM10 and PM2.5 showed strong seasonal influence of biomass open burning while BC distribution showed the influence of urban activities in big SEA cities. Daily average PM10 constructed from the hourly concentrations were obtained from the automatic monitoring stations in three SEA large cities, i.e. Bangkok, Kuala Lumpur and Surabaya for model evaluation. The daily observed PM2.5 and BC concentrations obtained from the Improving Air Quality in the Asian Developing Countries (AIRPET) project for 4 cities (i.e. Bangkok, Hanoi, Bandung, and Manila) were also used for model evaluation. In addition, hourly BC concentrations were taken from the measurement results of the Asian Pacific Network (APN) project at a sub-urban site in Bangkok. The modeled PM10 and BC satisfactorily met all suggested statistical criteria for PM evaluation. The modeled PM2.5/PM10 ratios estimated for four AIRPET sites ranged between 0.47–0.59, lower than observed values of 0.6–0.83. Better agreement was found for BC/PM2.5 ratios with the modeled values of 0.05–0.33 as compared to the observation values of 0.05–0.28. AODEM (extended aerosol optical depth module) was used to calculate the total columnar aerosol optical depth (AOD) and BC AOD using the internal mixing assumption. The model AOD results were evaluated against the observed AOD by both AERONET and MODIS satellite in 10 countries in the domain. Our model results showed that the BC AOD contributed 7.5–12 % of the total AOD, which was in the same ranges reported by other studies for places with intensive emissions. The Part 1 results (this study) is used in Part 2 (Permadi et al., 2017a) which calculates the regional aerosol direct radiative forcing under different emission reduction scenarios to explore potential co-benefits for air quality improvement, reduction in number of premature deaths and climate forcing mitigation in SEA in 2030.

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