Developing an emissions processing system to estimate local inventories for air quality photochemical modelling.

Transcription

1 Developing an emissions processing system to estimate local inventories for air quality photochemical modelling. Palacios Cesar; Castro Luisa; Pérez-Peña Maria Paula; Ramirez Jhonathan; Galvis, Boris; Castillo, Andrés; Pachón Jorge E.* Universidad de la Salle, Centro Lasallista de Investigación y Modelación Ambiental CLIMA, Bogotá, Colombia *jpachon@unisalle.edu.co Phone: (+571) ext 2515 Oscar M. Casas Colombian Petroleum Institute, Ecopetrol Bucaramanga, Colombia 1. INTRODUCTION Emission inventories are a key component for air quality modeling. To develop such inventories, different factors need to be considered: source type, geographic location, source activity, emission factors, rate and temporal behavior of pollutants emitted. Chemical transport models (CTM) need speciated emission inventories for each source type (point, mobile, area). In particular, CMAQ, the photochemical model implemented in Bogota, requires these emission inputs in a netcdf-i/o API format. (Arasa, Soler, Ortega, Olid, & Merino, 2010) There are several tools that prepare emissions for CTM models, such as SMOKE (David, Parra, Nacional, & Ine, 2006) or HERMES (BSC, 2015). However, model implementation is linked to specific information available for the region in which the system is developed. Although databases are adaptable to the emission model requirements, the process is complicated and prone to discrepancies and uncertainties. Herein, we disclose a new emissions processing system designed for the information available for Bogotá city. The system has been designed to read databases from local authorities, handling information from vehicle fleet counts, industry data, and geographic location of sources, among others. 2. EMISSION PROCESSOR *Corresponding author: Jorge E. Pachon, Centro Lasallista de Investigación y Modelación Ambiental CLIMA. Carrera 2 # 10-70, Bloque A, Piso 6, Bogotá, Colombia. clima@lasalle.edu.co The emission processor estimates emissions based on emission factors and activity data. In addition, this tool performs a speciation process for particulate matter and volatile organic compounds using SMOKE gsref and gsprof profiles (CMAS, 2016a, 2016b). The final output of the system is entirely compatible with CMAQ modeling set-up, both on a spatial and temporal basis. The tool is developed in Python to facilitate reading and operation. It also has a modular design to allow independence during the development process and allow the execution of the tool as a pipeline of independent tasks. In the near future it is expected that the system can be used and operated by the local environmental regulatory agency, SDA. 2.1 INSTALLATION AND METHODS The tool is developed in Python language version 2.7, under the Rapid Application Development model (Steve McConnell, 1996), which allows to develop the functionality of the tool according to the changing requirements of the research group, with a limited budget for development. The source code of the project, its documentation and its installation instructions are available at github ( under terms of the MIT license. 3. MATERIALS The emission processing system is being created to estimate local inventories for mobile (MOB), re-suspended dust (RPM) and point (PNT) sources. These three emission categories contain sub-categories within their kinds. Two mobile types define MOB: on-road combustion emissions (CMB) and tire and brake wear emissions (TBW). The 1

2 inventory of RPM is defined by five source categories: the first two, relate to the resuspension of dust generated by vehicles on paved (PRD) and unpaved roads (URD). The third RPM category corresponds to the dust emission produced by building construction (BLD). Likewise, RPM also considers dust emission from the construction of roads (PRC), and the dust from material extraction of clay and sand (QRY). Similarly, PNT source is made up of three categories: industrial (IND), commercial (COM) and gas storage facilities (GFS) emissions (Figure 1). Figure 1 Emission Source Categories To develop emission inventories for each of these source types, the system requires input information in xlsx format. 3.1 General procedure for emission calculations. The input information corresponds to local databases that are provided from government agencies such as the SDA and the district mobility agency (SDM) of Bogotá (MINTRANSPORTE, 2015; SDA, 2015; SDM, 2014). In order for the system to run calculations, the input data must contain all identification codes (specified by source), latitude, longitude, column and row (of modeling domain) for each link, point or area that is meant to be calculated. Table 1 shows some of the required identification code names that must contain the input tables (.xlsx). Table 1 Required identification codes CODE NAME AND DESCRIPTION LAT: Latitud LON: Longitude COL: Domain Column ROW: Domain Row FID_Grid: Identification number for grid cell FID_LINK: Identification number for link road IDStation: Station number of vehicular counts CLASIFI_SU: Classification number for land use CODE NAME AND DESCRIPTION Shape_Lenght: Link road length IDW_Cs: Road surface silt loading Emissions are calculated by multiplying the emission factor (EF) with the activity factor (AF) of each source ( Error! No se encuentra el origen de la referencia.). Equation 1 E = EF AF However, in order to get values of EF for RPM (PRD and URD), the system has been programed with three specific equations for RPM (Table 2). These equations (Table 2) were designed to be applied in U.S. units 1, but in order to get results in metric units (units used in Colombia), the system has been instructed to perform all the necessary unit conversions. All the input information come into the system in metric units. EF for URD is calculated based on two road types: public and industrial. Equation 3a calculates the FE for public unpaved roads, which are characterized by the transit of public vehicles such as cars and buses, and Equation 3b calculates the FE for industrial unpaved roads, where heavy traffic passes by. Table 2 RPM emission factor equations Source PRD URD Where: Equation 2 Equation FE = K S l W 0.85 Equation 3a FE = k ( s 12 ) 1 ( v 30 ) 0.5 C Equation 3b FE = k ( s 12 ) 0.9 ( w 3 ) 0.45 K = particle size multiplier for particle size range (g/vkt) SL = road surface silt loading (g/m 2 ) W = average vehicle weight (metric tons) s = surface material silt content (%) v = mean vehicle speed (km/hr) Emission factors for BLD, PRC and QRY are set into the system as constant values since those 2

3 values were deducted from past monitoring campaigns in Bogotá 2. Likewise, EFs for PNT sources are also set as constant values obtained from the ten-year air decontamination plan of Bogota 3. The system uses those constant EF values to calculate emissions that are based on the calculated AF, that vary in length and time. The AF for PNT sources involves fuel consumption values and removal efficiencies, these values enter to the system as.xlsx files, as well. The system is in charge to execute only the multiplication of values to get the emission. See Equation 4. Equation 4 E = Comb EF (1 ER) Where: E= emission (g/hr) Comb = fuel consumption EF = emission factor ER= removal efficiencies EF for MOB sources follow a different approach since those are not calculated by the system nor set as constant values. MOB EF are deducted by a different emission model, called MOVES, which estimates specific emission factors based on the type of vehicle, fuel and technology. With that saying, it is understood that all the EF values for MOB are introduced into the system as inputs. MOB, PRD and URD are the emission sources that have gone under additional programming due to the complexity of their inputs and information processes. These three sources manage their calculations trough links and vehicular fluxes (discussed in detail further in this text). 4. EMISSION PROCESSOR STRUCTURE In order to create a helpful tool to calculate local emission inventories, a modular design has been implemented, considering that each module corresponds to a specific task or source. The main purpose of this design is to allow independent evaluation for each source. This modular structure enables the performance of the tool to be fast and friendly to use. 4.1 MODULE STRUCTURE The general flowchart showed in Figure 2 displays the flow process that the system follows. It can be noticed that there are three modules specifically for each source type: PNT, MOB and RPM. Yet, there is one prior module named Pre- Process (PRE), which is in charge of organize, distribute and process information as requested. The tool begins at its first module, PRE, which corresponds to the pre-process of the main input data needed to perform calculations for MOB and RPM, only. The module contains two sub modules; GIS and FLX. The first sub-module is where all the Geographic Information such as coordinates, grid Figure 2 Main structure of emission process cells, station numbers and slit loading values is located. For instance, GIS is in charge of handling all the information of segments roads (links) 3

4 coming from xlsx files. This incoming information is treated in advance by another geographical model called, ArcGIS. The GIS sub-module will only contain information regarding roads, which are detailed divided in six different types: paved, unpaved, Transmilenio (Bogota s public transport system that travels in an exclusive road line of the street), public unpaved and industrial unpaved roads. Meanwhile, FLX, the second PRE s submodule, holds the information regarding local vehicular fluxes in the applied case of Bogota. The FLX module upload the useful information from the input file (.XLSX) and process the information to prepare a required output file (.CSV) of vehicular fluxes. See Figure 3 different fuel types (gasoline, diesel, biodiesel, natural gas) (Table 3) Once the information is recognized, the system connects GIS and FLX sub-modules by means of a common identifier, named as, ID_Station. The purpose of this common identifier is to connect link information to a specific vehicular flux, corresponding to the station number called from the ID_Station. Table 3 MOB vehicular categories Original NewCategory Fraction Original NewCategory Fraction AL AL_DSEL >C5_DSEL AT AT_DSEL >C5 >C5_GAS B B_DSEL >C5_GNV BA BA_DSEL ESP_GAS BT BT_DSEL ESP ESP_DSEL C MB_DSEL ESP_GNV C2G_DSEL INT_DSEL C2G C2G_GAS INT INT_GAS C2G_GNV INT_GNV C2P_DSEL AUT_GAS C2P C2P_GAS AUT_GNV C2P_GNV CC_GAS C3-C4_DSEL L CC_DSEL C3-C4 C3-C4_GAS CC_GNV Figure 3. Vehicular flux Flowchart Basically, this module takes the information from stations where traffic counts have been monitored, and places the information in the right order for the system to operate. Meaning that, the system sums up the traffic counts that come discriminated by vehicular counts of 15min to leave vehicular counts every hour. This is done with the purpose of leaving vehicular information for the 24hrs of both week day and weekend per monitory station. The vehicular categories that come from the input information are kept as they come categorized from local authorities, for the case of RPM (PRD, URD) emissions. However, for the case of MOB emissions, these categories go to a further division base on fuel type. The system takes each original category and divide its vehicular count by the fractions of vehicles using C5 C3-C4_GNV TX_GAS C5_DSEL TX_GNV C5_GAS M M_GAS C5_GNV AL: feeder bus. AT: articulate bus. B: bus. BA: bi-articulated bus. BT: medium bus. C: van. C2G: large two-axle truck. C2P: small two-axle truck. C3C4: three and four-axle truck. C5: five-axle truck. >C5: bigger than five-axle truck. ESP: special bus. INT: intermunicipal bus. L: lightweight car. The information processed in PRE is then taken as input for the following source modules: MOB and RPM. These two modules calculate the emissions according to the corresponding emission and activity factors (Equation 1) for each sub-source. Sub-modules like PRC, BLD and QRY do not receive information in link format. That is why GIS sub-module is the only source of information that feeds to the remaining three categories of RPM, since GIS provide the coordinate location for those activities. In the other hand, PNT module does not depend on PRE module. Since this information 4

5 come into the system in an orderly data set. Local authorities provide all the input information for this module, regarding type of industry and type of fuel used in productive processes. As well as, information concerning types of fuels used in charbroiling and wood activities. The system identifies the fuel consumption value of each industry and use it for the calculation of the emissions. See Table 4. Table 4 PNT data base example Source Type Fuel consumption data Business ID Fuel type RESTAURANT C1 PEDDLING C10 MEAT RESTAURANT MEAT C100 C108 C102 WOOD WOOD WOOD Fuel consumption RESTAURANT C104 WOOD 2000 As mentioned before, EF for PTN come as constant vales into the system. Every PTN source: IND, COM and GFS have their own EF values based on the type of industry. These EF come distributed by contaminant species such as PM10, NOx, SO2, CO, CO2 and VOC, for the applied case of IND and COM. See Table 5. Table 5 Point Source emission factors for COM and IND EF PNT (COM) FUEL ID DESCRIPTION PM10 NOX SO2 CO CO2 VOC C CHARBROIDING W WOOD CC CHARBROIDING+CHICKEN CBP CHARBROIDING+BEEF/PORK CBPC CHARBROIDING+BEEF/PORK+CHICKEN CW CHARBROIDING -WOOD EF PNT (IND) Category PM10 NOX SO2 CO CO2 VOC ACD E+06 2E E+09 - ACF 1E+06 7E+06 2E E+09 - AHF E+06 2E E+09 - CC CC CG E+06 - CG E+06 - CG E+06 - CM CTA 6E+06 4E+06 3E E+06 - GLP E E+09 - HC HFA 3E+06 1E+06 3E E+06 - HG E+06 - HG E+06 - HG E+06 - HL HM In GSF sub-module, the calculations to estimate emissions from gas storage facilities including tank refilling and distribution processes are executed the same way that COM and IND, but using different EF values. See Table 6. All the modules mentioned above (MOB, RPM, PNT) include the algorithms to disaggregate the emissions hourly by day or by year. Additionally, the information is spatially allocated in a domain of 64 X 64 grid cells (1 km 2 ) in a subsequent module called GRD. Table 6 PNT source emission factors for GSF Activity DISTRIBUTION SPILLING FILLING EF PNT (GFS) FE (Ton/gal) E E E SUBMODULE STRUCTURE Within each sub-module, a general structure is found and applied. It consists in a primary folder called data, where all the incoming information and outcomes are located by the system (by category). Meanwhile, at the same structure level, the folder, src, is situated. This folder contains the programming source code and its executable (main.py). Further in this folder, the main function applied to each source case is found as core file. An example of a MOB submodule file is presented in Figure 4. Figure 4 General submodule structure Considering that the information input from MOB and RPM (only PRD and URD) modules come in a link level, the calculation outputs from these two modules is taken to another sub-module called LINK which places the information in a 5

6 format easily to grip if the results are desired to be used in a road segment level. This module can display results in both hourly and daily units for the calculations of EF, AF and emissions ether in grams or tons. 5. GRD MODULE Since the main goal of the tool is to develop the input emission for photochemical modeling purposes, all the modules are connected to a submodule called GRD (Figure 4) of emissions in order to add emissions on a cell level (the grid domain is a function of the modeling configuration). The system is programmed to connect information through a common identifier called, FID_Grid. With that, the emissions happening within a same grid cell (but different link road) are summed up to generate only one emission value by grid cell. See Table 7. Table 7 Grid process example each source type, the profiles that are being used correspond to SMOKE sources profiles (CMAS, 2016a, 2016b) that are griped from gsref files. SPC is also in charge of transforming the units of pollutants, from gaseous pollutants information to mol/s (originally calculated as a mass rate). Particles are converted from g/hr to g/s. These unit conversions are made in order to get full compatibility with the modeling requirement. Also, in order to fill the model s requirement, PM10 has to be desegregated into course particulate matter (PMC), which is the subtraction of PM10 minus PM2.5. Each pollutant is calculated in a separated file; thus a final sub-module UNO is used to perform a binding of all pollutant files to get only one complied file per source. Finally, the emission outputs are ready to be transformed into a netcdf format for each individual source. See Figure 5. FID_LINK FID_Grid LAT LON Emission (g/d) Total emission by grid cell Additionally, in this module, unit conversion from g/d to ton/yr can also be achieved. After information is ordered in GRD, all individual sources are taken to a next module called SPC that stands for speciation. 5. SPC MODULE At the time, given that Bogota does not possess its own chemical speciation profiles for 6. TOOL RESULTS AND EVALUATION As previously explained, the emission preprocessor provides the results in several levels from the different sub modules, it is possible to use emissions for a micro-scale model such as R-LINE (taking the output information from LNK). It is also possible to get information to run AERMOD (although further format configuration is required). As for the evaluation, three check points that verify the readability of data and results are performed. Since emissions can be calculated for Figure 5 RPM flowchart example different years, it is important to verify that the data used to generated emissions is coming from the correct year s information. For that, the system assigns the year to every file throughout the calculation process (Figure 6 Allocation year). 6

7 Figure 6 Allocation year Also, when vehicular fluxes are going to be used, the system picks the vehicular flux file that stands with the same year s name as the input data. Pollutants as PM10 and PM2.5 undergo a checking process for all sources after the SPC module. The system scans all result values making sure that information adds and has magnitude sense. If the system identifies a negative value, it automatically pops-out a message notifying that the process and data need to be verified by the operator. This is because emissions can never be negative; thus, negatives number cannot be sent to modeling. In addition to, the system also checks that the value obtained for PMC agrees with PM10, by summing PMC and PM2.5. Both results are compared, and if there exist any difference between values, the system displays another warning message for the operator to check. The programming code can be found in GitHub (Figure 7). Figure 7 GitHub QR 6. CONCLUSIONS The emission processing system to estimate local inventories for air quality photochemical modelling, has been completed with a modular structure that provides programming code for three main pollution sources such as mobile, point and re-suspended dust. It calculates emissions in units of grams and tons as time period required (seconds, weekdays, weekends, year). The system provides outcomes at four emission levels: link, grid, pollutant specie and vehicular category. Additionally, the tool allows the addition of any further functionality to the code if needed at any time. Arasa, R., Soler, M. R., Ortega, S., Olid, M., & Merino, M. (2010). A performance evaluation of MM5/MNEQA/CMAQ air quality modelling system to forecast ozone concentrations in Catalonia. Tethys, Journal of Weather and Climate of the Western Mediterranean, BSC. (2015). HERMES Emission Model BSC- CNS. CMAS. (2016a). SMOKE GSPRO: Speciation profile file. Retrieved from tation/2.2/html/ch08s05s02.html CMAS. (2016b). SMOKE GSREF: Speciation cross-reference file. Retrieved from tation/2.5/html/ch08s05s04.html David, M. C., Parra, A., Nacional, I., & Ine, D. E. (2006). Desarrollo de Metodologías para la Aplicación de Modelos de la Calidad del Aire a Nivel Nacional en México Reporte Final Aplicación del modelo SMOKE para generar el Inventario Nacional de Emisiones de México 1999 para modelación Preparado por Consulto. Python Software Foundation. (2016a) csv CSV File Reading and Writing Python documentation. Python Software Foundation. (2016b) os Miscellaneous operating system interfaces Python documentation. Python Software Foundation. (2016c) json JSON encoder and decoder Python documentation. Rodríguez, C. H. (2014). Módulo 1 Numpy - Curso de Python Científico, SDA. (2010). Plan Decenal de Descontaminación del Aire para Bogotá. Bogotá D.C. SDA. (2014). Informe del contrato 1467 de 2013 Desarrollo e implementación de un modelo de calidad del aire para Bogotá suscrito entre la SDA y la Universidad de La Salle. Bogotá D.C. US EPA. (1995). AP-42 Section Unpaved Roads. Steve McConnell (1996). Rapid Development: Taming Wild Software Schedules, Microsoft Press Books, ISBN REFERENCES 7