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Emission Inventories - Science topic
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Hello!
I am wondering how to measure the error of the emission factor in the LCA software database.
For example, I found the emission factor of cement in a LCA software, but the details indicated that the emission factor did not include the transportation process. So how do I quantify that part of the error into my research?
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Tier 1 method for emission inventory calculates the emission on the basis of amount of fuels burnt by directly calculating the amount of product using stoichiometric equation.
How much sulfur dioxide is produced in Kg's when 1 litre of Diesel is burnt by vehicles?
I run WRF-Chem to simulate PM emission over India and Russia using GOCART aerosol scheme (chem_opt=300). I originally only wanted to simulate dust without chemistry, thus I did not provide emission data using prep_chem_src utility (Freitas et al., 2010). Anyway, the results show not only dust, but also PM2.5, PM10 BC, OC, DMS, etc. How did WRF-Chem simulate these particulates without emission data (anthropogenic, biogenic, gocart bg etc.) from prep_chem_src?
Emission factor development
Vehicles operating at high altitude
I want to calculate (or approximate) mass loading by dividing a mass of emissions by a particular area. The units work (as in aerosol mass loading is in g/m2 and so is mass of emissions/area) but i want to confirm whether this is a valid calculation and if not why not.
Thanks!
I am using average-speed emission functions for NOx which are reported in the UK national atmospheric emission inventory. I am using these, alongside traffic flow and speeds data I got from elsewhere, to calculate vehicle emissions across a city and then simulate dispersion.
My understanding is that these functions (in the UK NAEI) are sourced from COPERT 4 (version 10.0).
We would like to better understand the data underlying the functions and as I could not find a clear documentation of that, I am looking for any documentation/experiences that may help answer my queries below:
- What is the data underlying the curves/functions development? (laboratory measurements, PEMS, remote-sensing, modelled emissions, a mix of what exactly??)
- How well do the reported curves fit the data? (do you report a measure of goodness of fit or error somewhere)
- How was the averaging done so as to come up with the data points underlying the curves/functions? (speed and emission rates averaged over a trip? Over a defined time period? Or traveled distance?)
- Is there a relation between COPERT and the instantaneous emission model PHEM?
I would very much appreciate your help with this.
Can anyone suggest me some articles regarding source apportionment of gaseous compound using EPA PMF 5.00. Moreover, It will be very helpful if someone share his/her experiences and difficulties in working with PMF 5.00 while apportioning sources of gaseous compound.
Thanks in advance. :)
given GT operates at higher temperature than ST. I accept the fact that fuel consumption is low in gas turbine per kWh and therefore air used will be low but is there any other reason..an emission difference of 1/10 th is observed hence the question
(NG - Natural gas, ST- steam turbine, GT-gas turbine)
Most meteorological models that simulate rain do not take a particulate emission inventory as input, as do chemistry-coupled meteorological models. Is there an improvement of rain prediction in the latter case? If meteorological models do not work with particle emissions injected into the atmosphere, do they assume some particles in atmosphere to predict rain?
Most inventory cover only the main emissions and do not provide details. I am considering all emissions. For instance under transportation - CO2, NOx, CO, NMVOCs, HC, PM10, N2O, CH4, SO2 and NH3.
The accuracy of areas of peat fires are important component will be impact on the result of emission from peat fires. Most of the approaches used in the determination of peatfires areas based on burnt areas that derived from hotspot. The problem, burnt area just represent areas burnt scar in the above ground, we didn't know these of peat fire or no.
Emission inventories are always associated with uncertainty? I need a software to carry out uncertainty analysis on sets of emission values. @RISK or CrystalBall or any other one will be highly appreciated.
Can anyone share the chemical speciation profiles for NMVOC emission (exhaust and evaporation) from cold and hot emissions from each vehicle type (car, van, taxi, pickup, truck, bus, and motorcycle) in different fuel uses (such as gasoline, gasohol, diesel, LPG, CNG)? There many profiles in SPECIATE database. Are there any recommended composite or simplified profiles?
e.g. the guides on animal husbandry focus on enteric fermentation but oversight the emissions from livestock buildings where the manure is stored for short periods. In addition, there are emissions from the all contaminated areas inside the barn.
I need to input the same on GIS platform and have socioeconomic and pollution receptor modelling and analysis.
I want to estimate dust emission deriving from quarrying, vehicle transit on dirty roads, stockpile operations, etc in a clay-processing industry. Estimated emissions will be used for atmospheric dispersion modelling.
The most used methodology is EPA's AP-42 Fugitive Dust section.
Nevertheless this approach seems very simple. The equations for estimating emissions are based on regression analysis of few measured data from some case studies in the USA, so they are "statistical" models without any mechanistic foundation. It is problematic to apply the same set of equations in other sites with probably different characteristics.
Does anyone know other methodologies/equations for this kind of emission estimation?
At present I must exclude experimental campaign to measure emissions, so I need to refer to the emission factor approach.
I am trying to estimate the amount of CO2 reduced by EVs by comparison of per capita emission by other vehicles that a person will use to commute in the absence of EV. I will be using the conversion value for CO2 emission by combustion of gasoline and diesel given by International Association of Public Transport.
In your view is this the appropriate method?
What other statistical tools or models (if any) I can use for this study ?