Fugitive PM 1 0 John W atterson John W atterson ( w ith thanks to John Abbott) ( w ith thanks to John Abbott) W hat’s in this presentation • Guidance • Monitoring • Modelling • Case studies of modelling 1
W hen not to w orry about fugitive PM 1 0 • “Short tem construction works do not normally need to be considered for the purposes of Review and Assessment” • LAQM.TG4(00), Section 8.36, Page 115 W hich m onitor to use • TEOM (Tapered Element Oscillating Microbalance) • BAM (Beta Attenuation Monitor) • Gravimetric • All used in the national networks • Partisol (not recommended) 2
Monitoring at Stage 3 • LAQM.TG4(00) defines the relevant criteria and sampling periods – Ideally monitor for 1 year with 90% data capture – minimum 6 months is being advised (summer/ winter) if comparing with national network sites – 3 months will often do (but consider wind direction) W hen is m onitoring not required? • If future predicted concentrations well below objective, LAs can conclude – objective unlikely to be exceeded – even if model accuracy only within 50% • Recommendation in LAQM.TG4(00) to monitor in mixed urban areas to validate model • At Stage 3 LAs need confidence in their results 3
Can you use som eone else’s m onitoring data? • Can you use monitoring data from neighbouring areas / sources to validate your model? • For emissions from traffic, yes, but – area that has been modelled must be ‘identical’ to the one under investigation – (interpret as very similar in nature!) • For point sources – sources need to be similar in nature For fugitive PM 1 0 • For fugitive sources – each source unique, and local meteorological effects likely to be importan so can’t really apply this approach – you’ve probably got to do your own monitoring 4
QA/ QC of data - it’s essential! • Is it necessary to bother with detailed QA/ QC when it’s – time consuming – costly – and the data ‘seems reasonable’ • Yes, because the data cannot be used to validate the model without QA/ QC • An examples of why QA/ QC critical to follows ... Data w ithout QC - m onitoring of fugitive PM 1 0 PM 10 concentrations ( µ µ g m -3 TEOM), without any QC, from fugitive µ µ emissions from a quarry 500 250 µ g m -3 ) 0 PM 10 concentration ( µ µ µ Mar-99 Jun-99 Sep-99 Dec-99 Mar-00 Jun-00 -250 -500 -750 -1000 Time 5
Monitoring for fugitive PM 1 0 PM 10 sampler Housing (nearest receptor) Met. meteorological station PM 10 Met. Quarry 400 m Working face - area of maximum Prevailing wind activity PM 10 direction Monitoring for fugitive PM 1 0 • Ideally site PM 10 samplers upwind and downwind close to nearest receptors – but in reality, one sampler upwind • Meteorological data is important – perhaps use data recorded at a nearby site? • PM 10 filters can be retained for microscopic analysis • Sensitive receptors all > 400 m from active face? Probably no monitoring needed. 6
W here can you turn to for m ore help? • MONI TORI NG help desk – Operated by The National Environm ental Technology Centre – Telephone 01 235 46 3 35 6 – Em ail aqm .helpline@aeat.co.uk w eb site w w w .aeat.co.uk/ netcen/ airqual/ w elcom e.htm l • MODELLI NG help desk – Operated by Stanger Science and Environm ent – Telephone 018 1 2 56 4 972 – Em ail m odelhelp@stanger.co.uk – w eb site w w w .stanger.co.uk • LOCAL AI R QUALI TY POLLUTANTS' SPECI FI C GUI DANCE help desk – http:/ / w w w .uw e.ac.uk/ aqm / review – check the FAQ bit Sum m ary Ensure Ensure Ratify the data Ratify the data cost effective m onitoring " cost effective m onitoring " screen ! " screen ! " at the correct locations " at the correct locations " scale ! " ( public exposure) scale ! ! " ( public exposure) review " over the correct period review " " over the correct period " equipm ent m aintained " equipm ent m aintained " & calibrated & calibrated Correct m onitoring to support assessm ent Use to help validate the Take tim e to analyse Use to help validate the Take tim e to analyse m odelling and define data and present in a m odelling and define data and present in a areas of exceedence useful form areas of exceedence useful form 7
Case studies Fugitive PM 1 0 em issions John W atterson, Beth Conlan ( w ith thanks Bob W ade, form erly King's Lynn & W est Norfolk Borough Council) Goods yard - fugitive PM 1 0 issue • Overview – Local Authority wishes to build housing close to a railway – but there are several sources of fugitive PM 10 close to the railway line (aggregate delivered by railway) – how close could they build the houses without exceeding the most stringent PM 10 objective? 8
Main sources of em ission • Main sources of emission from aggregate handling operations are – Loading of aggregate onto storage piles (batch or continuous drop operations); – Equipment traffic in storage area; – Wind erosion of pile surfaces and ground areas around piles; – Loadout of aggregate for shipment or for return to the process stream (batch or continuous drop operations Site layout & m odelled 9 0 th percentile 2 4 hour average PM 1 0 concentrations, µ µ g m -3 µ µ 39100 Wet batching plant 39050 Ideal monitor 39000 location 38950 Proposed housing Bottom drop feed hopper 38900 Roadstone Northing, m coating plant Aggregate pile 38850 Three sided Aggregate pile aggregate 38800 storage Railway lines 38750 ‘Topmix’ dry batching plant ACTUAL 38700 monitor location 38650 38600 28500 28550 28600 28650 28700 28750 28800 28850 28900 Easting, m 9
Predictive em ission factor equations for each type of source taken from US EPA AP4 2 em issions database • The quantity of dust emitted from vehicle traffic on a paved road ... • The quantity of particulate emissions from batch or continuous drop operations ... • The emissions generated by wind erosion of stockpiles ... • Hourly meteorological data was needed (March-May 2000) • ( See me after if you want the detailed methodology and equations! ) Selection of m onitoring site • This was a problem • The ‘ideal’ location had no power supply • So another location was selected - location of monitor limited by where there was power • Welcome to the ‘real world’! 10
Receptor locations • The dispersion model was used to calculate the contribution from each modelled source – to concentrations at receptor locations spaced on a regular grid – extending approximately 500 m from the goods yards – with grid nodes at 50 m intervals. Model validation for PM 1 0 • The monitoring data obtained at the Goods Yard was limited by duration and by the location of the monitoring station • Hourly average modelled contributions from the handling operations compared with the excess concentration at the monitoring station • The ‘excess concentration’ was calculated as the measured concentration at Crawley Goods Yard minus the measured concentration at the Rochester automatic network site (nearest background site) 11
Data selection criteria for m odel validation • The wind was from the sector between 300 and 30 degrees • Meteorological data was available from Gatwick Airport • The Crawley Goods Yard monitoring site was operating • The Rochester monitoring site was operating • There was a train delivering material to the Aggregates on that day • The m odelled concentration was not zero • The m easured concentration at Goods Yard was greater than that at the Rochester site Model perform ance • Measured ‘excess concentration’ plotted against modelled fugitive PM 10 prediction • Model overestimating PM 10 concentrations by several µ gs • Considerable uncertainty in the model predictions as uncertainties inherent in model predictions of emissions and pollutant dispersion and the short period of the model run 12
W hich concentration to use as exceedence lim it? • Uncertainty (one SD) resulting from the short period of the model run (from period to annual mean monitoring data comparison) was estimated to be 10 µ g m -3 (gravimetric) • The model validation indicates that the model overestimates the contribution from the handling operations • It is thus likely that the 40 µ gm -3 and 60 µ g m -3 isopleths provide reasonable upper and lower bounds on the expected area of exceedence of the 24 hour PM 10 objective Grain handling at a dockyard - fugitive PM 1 0 • Dust nuisance complaints from grain handling operations • TEOM monitoring for PM 10 at a location nearby suggests exceedences of current 24 hour objective • Modelling suggests exceedences of the 24 hour PM 10 objective 13
Data needed for m odelling of fugitive PM 1 0 • Shipping movements and cargo handled • Air quality monitoring – continuous TEOM to validate the results of the model against • Appropriate dispersion model • Meteorological data Monitoring location 14
Unloading grain from lorry prior to ship loading Grain loading / unloading 15
Recommend
More recommend
