REPORT ON THE EMISSION INVENTORY DATABASE FOR KZN PROVINCE

REPORT ON THE EMISSION INVENTORY DATABASE FOR KZN PROVINCE by Jay Puckree and Dr T Fasheun Department of Agriculture, Environmental Affairs and Rural Development Sub-Directorate: Air Quality Management and Climate Change p j g e-mail: puckreej@kzndae.gov.za Presentation at DANIDA-UEMP 21-22 May 2009, Cape Town CONTEXTUAL SETTING The Need for Emission Inventory Database The Planning hierarchy Study Area Targeted Sources and Pollutants Emission Inventory Design Data Acquisition D t A i iti Methodology Emission Estimation Overall Results Limitations/Opportunities Conclusions The Need for Emission Inventory Database To gather base-line information on air quality Sources of emission Emission dispersion Sinks of emission For continuous data update Such information to be used to develop management and control strategies t d l t d t l t t i to calculate the spatial distribution of pollutants As inputs into State of Environment Reports for baseline data for modelling for research purposes For location of monitoring stations Reference data spatially on a GIS Information to inform Provincial AQMP Serve as a base which subsequent inventories can be compared with in order to evaluate regulatory programmes How does the emission inventory fit into the planning p g Clean Air process? AQMP CONTROL MODELING MONITORING EMISSION INVENTORY STUDY AREA OF THE EMISSION INVENTORY PROJECT SOURCES Stationary and point source emissions - industries principally schedule processes sugar mills; breweries; foundries; paper mills etc. ill b i f di ill t Emitters of Criteria pollutants esp SO2 and PM10 Mobile - Motor Vehicles - Aircraft Area Sources - Veld; Stockpiles - Sugar Cane Emission Inventory Design Assessed the scope and objectives of the study with the Consultants The sources identified in the study considered the criteria pollutants including benzene The grouping of contaminants were also considered as: - Known or suspected Carcinogen - High irritation or system effects ode ate tat o - Moderate irritation - Low irritation This will indicate the sampling techniques Emission Inventory Design (Contd) Develop point source questionnaire in accordance with information requirements of DAEA. Assist industry in completion of point source questionnaire. Typical i f T i l information requested: ti t d - Company information - Fuel consumption data; production data - Stack and Emission data (SO2, NO/NOx/NO2, PM10, CO) (SO2 NO/NOx/NO2 PM10 - Cleaning device data Extensive interaction with local government and DAEA y p p authorities to identify top 10 to 15 polluters in each municipality. Issued questionnaires to industries to complete. Visited industries that require technical assistance in completing questionnaire. questionnaire PROCEDURES TO OBTAIN INFORMATION Develop questionnaire in accordance with requirements of DAEA. Assist industry in completion of point source questionnaire. Typical i f T i l information requested: ti t d - Company information - Fuel consumption data and production data - Emission data (SO2, NO/NOx/NO2, PM10, CO) (SO2 NO/NOx/NO2 PM10 - Stack data; Cleaning device data Extensively interact with local government and DAEA y p p p y authorities to identify top 5 polluters in each municipality. Issued questionnaires to industries to complete. Visited industries that require technical assistance in completing questionnaire Consultation with Fire Departments C lt ti ith Fi D t t DATABASE ENTRIES Table Name CityNames CompanyDetails Districts FuelBurnData LocalCouncils ProcessFuelData ProcessFugitiveEmissionData Description List of cities/towns within Local Councils General details of companies included in emissions inventory List of district councils within KZN Fuel burning data with schedule of operation, etc. List of local councils within district councils Consumption rates and types of fuel used in processes Emission data measured/calculated that does not exit a specific stack ProductionData SectorCode StackData Production rates of processes Divides industries by type of sector Emission data for stacks, including physical parameters and cleaning device methods METHODOLOGY Fuel Consumption and Emission Factors from US-EPA, AP- 422 used Personal Comm Questionnaires - Mass Balance - Measured Data Historical Information Fire Departments Modelled Values from Sugar Cane Burning Methods to determine point source emission rates 1. Measurement: Stack monitoring, continuous emissions monitoring. Most accurate but costly. 2. Mass Balance Mass in = Mass out 3. Estimation Emission factors- research by US-EPA factors US EPA Traffic Emissions Determine number of households per area from town planning records Estimate number of vehicles per area using number of households as a basis Obtain data fro local traffic department p Estimate average gasoline consumed per vehicle Apply emission factors to determine quantities of pollutants emitted in a given area Industrial Emissions Define areas in KZN Identify industries both Schedule and NonSchedule Process industries Names of all Industries captured under each Municipality and sector classification in Excel p y format Determine consumption of fuel/hfo for various industries Registration of fuel burning appliances Application of emission factors pp AIRCRAFT EMISSIONS ICAO Data bank emissions for aircraft engines- calculation of total emissions based on no of engines and LTO and TIM Use of emission factors in pollutants per kg of burnt fuel p p g together with fuel flow in kg per second EF provided for take of; climb out; approach and idle. Uncertainty of aircraft types; reverse thrust landing and taxi in and taxi out times To account for engine deterioration NO emissions have increased by 4.5%, others increased by 4%. PM10 emissions relatively sparse and where they do exist is i i l ti l d h th d i ti relatively old. Estimates of CO2 emissions have been calculated from total fuel use Summary of Aircraft Emissions y t/y Mode Take off HC NOx CO 0.57 2.73 53.31 7.21 PM10 0.12 0 12 0.03 0.01 0.06 0.03 0.25 0 25 17.80 17 80 CO2 0.36 44.38 2.70 0 36 44 38 2 70 Climbout 0.08 6.86 Approach 0.14 2.98 h 8 Idle APU Total 3.38 9.12 0.41 3.14 4.37 66.48 66.52 4 37 66 48 66 52 AREA SOURCE: SUGAR CANE Four regions under sugar cane cultivation viz Zululand; Midlands; North and south Coasts. Total area 377,000 hectares Info obtained from SASA 2006 data Data used to calculate sc burning emissions using emission factors obtained from Australian NPI Main pollutants PM10;CO; NOX and VOC’s. GIS Map concentrations in the 10 Districts and Metro Map will show concentrations of CO, CO2, SO2, NOX, PM10, VOC PM10 VOC, Pb and other unspecified pollutant in each district Summary tables in ArcView 3.3 Program For Point and area source emissions seven maps were produced using Natural Breaks Method. One p p map for each pollutant. Similar method was applied to map pollutant from mobile sources. Five maps showing emissions of CO, SO2, NOX, PM10 AND VOC were produced BREAKDOWN OF RESULTS PM10 tons/yr, 10617.322, 3% NOX tons/yr, 2246.8716, 1% LEAD tons/yr, 0.60929, 0% TOC tons/yr, 396.876, 0% SO2 tons/yr, 110225.869, 36% CO t tons/yr, 186770 594 60% / 186770.594, SO2 FROM POINT SOURCES T/Y Umzinyathi, 0, 0% Umgungundlovu, 364.19, 0% Amajuba, 2207.912, 2% Umkhanyakude, 0.42, 0% Ugu, 26.916, 0% Uthukela, 532 38 Uthukela 532.38, 0% Zululand, 0.16, Z l l d 0 16 0% Uthungulu, 22523.2, 20% Ilembe, 2409.02, 2% Sisonke, 0, Sisonke 0 0% Ethekwini, 82161.671, 76% VEHICLE EMISSIONS TOC tons/yr, 127395.4805, 13% , , 0% PM10 tons/yr, 40033.83105, 4% NOX tons/yr, 132357.7168, 14% SO2 tons/yr, 10765.08695, 1% CO tons/yr, 666777.6178, 68% PM FROM VEHICLES Umgungundlovu, 2646.10, 7% Umzinyathi, 2663.45, 7% Amajuba, 638.61, 2% Umkhanyakude, 990.51, 2% Ugu, 994 00 2% U 994.00, Uthukela, 6395.96, 16% Zululand, 905.11, 2% Uthungulu, 9341.65, 23% Ethekwini, 13351.77, 34% Ilembe, 2036 16 5% Il b 2036.16, Sisonke, 70.51, 0% OVERALL RESULTS-CO Non-mobile, 104.8065468, 0% Industrial , 186513.894, 22% 186513 894 Vehicles, 666498.3821, 78% OVERALL RESULTS-NOX Non-mobile, 209.6130936, 0% Industrial , 2246.8716, 2% Vehicles, 105833.5871, 98% OVERALL RESULTS-SO2 Vehicles, 5222, 5% Non-mobile, 138.3446418, 0% Industrial , 109738.669, 95% SUCCESS AND CHALLENGES First study in the country and a learning curve to our Department Identified target locations for ambient air monitoring systems as well as type of analyzers required for each area. Baseline air quality info now available for air specialist studies in the EIA Process Most industries produced sufficient point source data from their monitoring system Process validated model outputs CHALLENGES Obtaining data from industries presented a major challenge. Info to be outside of public domain Absence of Model data in some instances viz Vehicle emissions Data Limitations Lack of current data e.g. traffic count Model required vehicle class e.g. diesel eg or petrol Vehicle age Road gradient, etc It was difficult to get this information. Fuel base approach was used. THREATS TO THE REALIZATION OF THIS PROJECT Refusal to disclose information Non-co-operation from certain sectors Industries not making information available on time Lack f L k of capacity at certain industries to i i i d i complete the questionnaire OPPORTUNITIES REALIZED Capacity building for the air quality practitioners Sensitize governance officials on this type of science Footprint of the location of Schedule Activities and vulnerable areas to inform the EMF Hot spots identified and validated Important tool for planning purposes including the EIA process Areas that may be violation of AQM Standards Expected non-attainment areas nonDurban, Richards Bay, PMB and Newcastle , y, Possible other non-attainment areas: nonUmkomaas Mandeni Empangeni p g Cato Ridge Estcourt UPDATE OF EMISSION INVENTORY Pers Comm with Industries Accessing registration Database at DEAT Mass balance Obtaining Info via the AEL’s and EIA Sugar Mills and Saw Mills Challenge: Staff Constraints CONCLUSION Emissions inventory is considered to be one of the major tools for air quality management planning Completing this study will help us to focus on important pollutants and sources Understanding of model performance and validation Information will assist Health Risk Assessment Enforce reduction strategies Input to assessment studies The traffic t di Th t ffi studies will also assist in the identification ill l i t i th id tifi ti of the number of diesel driven vehicles for planning purposes Continuation of the studies ACKNOWLEDGMENTS The emission inventory data was collected and processed by ZES Consultants Thankful for the industries and others who supplied data and information Grateful to the municipalities for their cooperation DANIDA Funding for all their financial support THANK YOU AND GOD BLESS