John W atterson & Beth Conlan Conlan John W atterson & Beth - - PowerPoint PPT Presentation

Local improvement needed in Local improvement needed in air quality and source apportionment air quality and source apportionment

National Environmental Technology Centre National Environmental Technology Centre

John W atterson & Beth John W atterson & Beth Conlan Conlan

What’s in this presentation What’s in this presentation

g g What are your duties in an AQMA

What are your duties in an AQMA

g g How do you approach this task

How do you approach this task

g g Working out what sources are important

Working out what sources are important

g g How much of an improvement is needed

How much of an improvement is needed

g g Case studies

Case studies

− assessment of road traffic and industrial releases − setting the scene for effectiveness

• of some traffic management schemes
• and cost benefit/effectiveness studies

What are your duties after declaring an What are your duties after declaring an AQMA? AQMA?

g g Section 84(1) of the Environment Act ...

Section 84(1) of the Environment Act ...

− LAs must carry out further assessment of existing and likely future air quality in an AQMA

g g You need to assess

You need to assess

− how much of an improvement in air quality needed − the extent to which different sources contribute to the problem

Outcome of this further Review and Outcome of this further Review and Assessment Assessment

g g Clear picture of the sources that LAs

Clear picture of the sources that LAs can control or can control or influence influence

g g Should ensure action plans strike a balance between

Should ensure action plans strike a balance between

− contribution from the LA − contribution from other sectors

Where the improvements should be Where the improvements should be directed directed

g g Effective targeting of resources

Effective targeting of resources

− cost effective − proportionate

How long do you have to do this? How long do you have to do this?

g g Section 84(2) of the Environment Act …

Section 84(2) of the Environment Act …

− Must report within 12 months of designating an AQMA − Need to consult on this further Review and Assessment (Stage 4)

Basic information needed Basic information needed

g g Hopefully from your Stage 3 R&A ...

Hopefully from your Stage 3 R&A ...

g g Pollutants

Pollutants

g g Averaging periods

Averaging periods

g g Principal sources

Principal sources

g g Geographical extent of exceedence

Geographical extent of exceedence

Source apportionment Source apportionment

g g Major element - blaming others!

Major element - blaming others!

✓ ✓ Combination of sources

Combination of sources

• road, industry (Part A/ B), rail, airport, domestic fuel

✓ ✓ Road - what type of vehicle is main source

Road - what type of vehicle is main source

• HGV, buses, cars?

✓ ✓ Sources outside the district

Sources outside the district

• can you control these effectively?

Improvement in Air Quality Improvement in Air Quality

g g How to assess how much is needed?

How to assess how much is needed?

✓ ✓ modelling required

modelling required

✓ ✓ look at emissions reduction e.g. in

look at emissions reduction e.g. in NO NO x

x

to deliver a to deliver a NO NO 2

2 reduction at a receptor

reduction at a receptor

✓ ✓ background concentrations

background concentrations

✓ ✓ not a linear relationship for NO

not a linear relationship for NO x

x to NO

to NO 2

2!!

!!

✓ ✓ needs scenario testing

needs scenario testing

Source apportionment - how precise? Source apportionment - how precise?

g g How precise?

How precise?

✓ ✓ based on model output

based on model output

✓ ✓ meteorological influences

meteorological influences

✓ ✓ source apportionment should be calculated in

source apportionment should be calculated in rough percentage terms (nearest 5-10%) rough percentage terms (nearest 5-10%)

✓ ✓ Main aim is to be reasonable and proportionate in

Main aim is to be reasonable and proportionate in reducing emissions reducing emissions

Do all the concentrations of the Do all the concentrations of the sources just add up? sources just add up?

g g Annual averages

Annual averages

− e.g. NO2 and PM10 − concentrations from different sources can simply be summed

g g Objectives with short averaging periods and

Objectives with short averaging periods and percentile exceedences percentile exceedences

− high percentile concentrations (e.g. 99.8th) cannot really be added − impacts from sources unlikely to coincide temporally

• r spatially

Pollutants with shorter averaging Pollutants with shorter averaging period objectives period objectives

g g Pollutants

Pollutants

− CO (8 hour objective) − PM10 (24 hour objective) − SO 2 (24 hour objective) − NO2 (1 hour objective) − SO 2

(15 minute and 1 hour objectives)

SO SO2

2 (24 hour objective)

(24 hour objective)

g g Major source

Major source - probably industrial

• probably industrial

g g Background

Background

− vehicle, domestic and remote industrial − TG4 suggests 24-hour background can be local annual average concentration in 2004 (half of 1996 value)

g g Industrial contribution

Industrial contribution

− need to use dispersion modelling − sequential modelling to assess impacts from individual sources

PM PM10

10 (24 hour objective) &

(24 hour objective) & CO (8 hour objective) CO (8 hour objective)

g g Assuming that the approach in LAQM.TG4(00) has

Assuming that the approach in LAQM.TG4(00) has been followed ... been followed ...

g g … for contribution from sources and backgrounds

… for contribution from sources and backgrounds

g g Then relative contributions can be derived by

Then relative contributions can be derived by disaggregating disaggregating the total concentration the total concentration

NO NO2

2 (1 hour)

(1 hour) SO SO2

2 (15 minute and 1 hour)

(15 minute and 1 hour)

g g Relative contributions can be derived by

Relative contributions can be derived by disaggregating disaggregating the total concentration the total concentration

g g Industrial contribution

Industrial contribution

− need to use dispersion modelling − sequential modelling to assess impacts from individual sources

Sources of data for source Sources of data for source apportionment assessment apportionment assessment

g g Predictions on a national basis

Predictions on a national basis

g g Using the NAEI to get an idea in you local area

Using the NAEI to get an idea in you local area

g g Taking apart the results from modelling studies at

Taking apart the results from modelling studies at Stage 3 - what reduction is needed? Stage 3 - what reduction is needed?

g g Effects of some traffic management schemes - can

Effects of some traffic management schemes - can they achieve the reduction? they achieve the reduction?

UK Road Transport Emissions of PM10

10 20 30 40 50 60 70 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025

20% diesel car sales; Central 1997 NRTF; Assumes Euro III emission reductions for all new vehicles in 2001, Euro IV reductions for cars, LGVs only in 2006; Fuel standards reduce emissions in 2000 & 2006 for all vehicles

Emissions (ktonnes

Motorcycles LGV Buses HGV Cars DERV Cars petrol

Emission projections on a national Emission projections on a national basis basis

NO NOx

x emissions (1970-1996)

emissions (1970-1996)

1970 Emissions

Public Power 30.1% Industry 20.1% Domestic 2.6% Road Transport 32.2% Other Transport 12.1% Other 2.9%

1996 Emissions

Public Power 21.8% Industry 13.6% Domestic 3.6% Road Transport 46.9% Other Transport 11.8% Other 2.3%

Public Power Industry Domestic Road Transport Other Transport Other

Using the NAEI emissions inventory Using the NAEI emissions inventory

Limitations of NAEI emissions data Limitations of NAEI emissions data

g g Data given in tonnes/ year/ km

Data given in tonnes/ year/ km2

2

g Gives relative proportions - can determine what are

the most important sources

g g In the end, need concentrations -

In the end, need concentrations - µ µg/ m g/ m3

3

g Require some modelling to convert

tonnes/ year/ km tonnes/ year/ km2

2 to µ

µg/ m g/ m3

3

g g Consider annual mean nitrogen dioxide

Consider annual mean nitrogen dioxide

g g Up to three possible components ...

Up to three possible components ...

Using the results from modelling Using the results from modelling studies studies

traffic (and/or) industrial Background (2005) (~5 to 25 µg/m3)

+

Background concentrations Background concentrations

g g To calculate the improvement, need to know the

To calculate the improvement, need to know the background in the absence of any traffic effects background in the absence of any traffic effects

g g In these examples, determined from an urban

In these examples, determined from an urban background model background model

g g So the reduction necessary in

So the reduction necessary in µ µg/ m g/ m3

3 from

from traffic/ industry can be calculated traffic/ industry can be calculated

Examples Examples

g g Exceedences of

Exceedences of annual mean NO annual mean NO 2

2 objective at

houses

− near motorway − near junction − in town centre

g g Exceedences of

Exceedences of 24-hour PM 24-hour PM10

10 objective at houses

− near foundry in town centre

g g T hink about options for reductions in N O

T hink about options for reductions in N O 2

2 ….

Houses close to a busy motorway Houses close to a busy motorway

g g Synopsis

Synopsis

− houses close to a busy motorway − within 30 m − high background in one section − traffic split: 83% cars, 17%HGV − reduction in NO 2 necessary probably applies for some distance along motorway

Concentrations of NO Concentrations of NO2

2 predicted in

predicted in 2005 close to a motorway 2005 close to a motorway

Minimum reduction in NO Minimum reduction in NO2

2 needed

needed

g g 47

47 µ µg/ m g/ m3

3 to 40

to 40 µ µg/ m g/ m3

3 = 7 µ

µg/ m g/ m3

3

g Breakdown of NO 2 sources

− background 23 µg/ m3 (from modelling) − traffic 24 µg/ m3 (from modelling)

g Maximum amount permitted from traffic

(40 - 23) = 17 µ µg/ m g/ m3

3

Road junction Road junction

g g Synopsis

Synopsis

− two busy roads − more complicated NO 2 contour pattern − houses close to roads − size of exceedence variable

Concentrations predicted in an urban Concentrations predicted in an urban area area

Minimum reduction in NO Minimum reduction in NO2

2 needed

needed

g g Size of exceedence varies according to the NO

Size of exceedence varies according to the NO 2

2

concentration field concentration field

g g Need to assess improvement needed

Need to assess improvement needed

g g In other words, need to address area(s) with

In other words, need to address area(s) with maximum maximum exceedence exceedence

g g And ensure objective met at this location

And ensure objective met at this location

Town with busy trunk road and several Town with busy trunk road and several A roads A roads

g g Synopsis

Synopsis

− extremely busy town centre, normally congested for most of the day with slow moving traffic − up to 11% HGV − high volume of traffic (AADTF ~60,000 in 2005) along one road link − slow moving (speed ~20 kmph) − exceedence of annual mean NO 2 objective predicted by modelling at several locations

Concentrations of NO Concentrations of NO2

2 in 2005 - several

in 2005 - several areas of exceedence areas of exceedence

# # #

Exceedences probable at receptors Exceedences probable at receptors close to roads close to roads

Minimum reduction in NO Minimum reduction in NO2

2 needed

needed

g g 46

46 µ µg/ m g/ m3

3 to 40

to 40 µ µg/ m g/ m3

3 = 6 µ

µg/ m g/ m3

3

g Breakdown of NO 2 sources

− background 18 µg/ m3 (from modelling) − traffic 28 µg/ m3 (from modelling)

g Maximum amount permitted from traffic

(40 - 18) = 22 µ µg/ m g/ m3

3

Working out reductions needed in NO Working out reductions needed in NOx

x

g g Assumptions

Assumptions

− receptor at ‘background’ location − modelled total NO2 44 µg/ m3 − need to reduce to ... 40 µg/ m3 − can only reduce road traffic contribution

g Assume relative source contributions of ...

− road traffic 50% − industry 20% − background 30% (unaccounted sources)

NO NOx

x reduction needed

reduction needed

g NOx reduction needed

NO2 NOx

− modelled total 44 µg/ m3 97 µg/ m3 − need to reduce to ... 40 µg/ m3 85 µg/ m3 − by difference … 4 µg/ m3 12 µg/ m3

Percentage NO Percentage NOx

x reduction needed

reduction needed

g Percentage NOx reduction needed of the total NOx

% reduction = 1-[(97-12)/ 97] = 12 %

g Traffic contributes 50% of total NOx

= 0.5 * 97 = 49 µ µg/ m g/ m3

3

Percentage improvement in NO Percentage improvement in NOx

x from

from road traffic needed road traffic needed

g g Require

Require 12 µ µg/ m g/ m3

3 reduction in NO x

g Traffic contribution needs to be reduced by

49 µ µg/ m g/ m3

3 - 12 µ

µg/ m g/ m3

3 = 37 µ

µg/ m g/ m3

3 NOX

g % improvement NO x = [(49 - 37)/ 49]*100

= 24 %

Industrial release - Industrial release - Foundry in a town Foundry in a town

g g Synopsis

Synopsis

− developer wants to build houses and light commercial site, but close to …. − foundry making die-cast products

• particulate material discharged through many

extraction systems − existing bus depot

• with plans to expand

− railway

Approach to source apportionment Approach to source apportionment study study

g g Background

Background

− monitoring nearby − and used LADS background model

g g Roads

Roads

− taken into account increase in traffic from proposed development − emission factors taken from DMRB

Approach to source apportionment Approach to source apportionment study study

g g Rail

Rail

− emission factors from the NAEI (g/ km)

g g Buses

Buses

− taken into account proposed increase in bus movements from expansion of depot − calculated emission rates (from DMRB) per unit time from stationary and manoeuvring buses for low speeds − allowed for change in fleet composition with time and so reduction in PM10 emissions

EU directives reducing emission rates EU directives reducing emission rates with time with time

Table 4.7: Emission rates from stationary and manoeuvring buses Pollutant Emission rate g/ hour per bus according to EU directive: Pre 91/ 542 91/ 542 91/ 542/ II EuroIII EuroIV Oxides of nitrogen 133 93 67 47 33 Particulate matter, PM10 15 10 6 4 3 Carbon monoxide 138 69 55 47 39 Hydrocarbons 161 150 52 52

(In this study, assumed buses will meet Stage II levels by (In this study, assumed buses will meet Stage II levels by 2004/ 5) 2004/ 5)

Annual average PM Annual average PM10

10

µ µ µ µ µ µ µ µg m g m-3

• 3

in 2004 at receptors in 2004 at receptors

Receptor Foundry 1 Foundry 2 Railway Bus Stn Roads background TOTAL 1 0.04 2.27 0.37 0.03 1.25 21.5 25.46 2 0.03 4.23 0.37 0.05 2.28 21.0 27.96 3 0.03 4.77 0.43 0.04 2.05 20.9 28.22 4 0.04 5.64 0.52 0.03 1.96 20.8 28.99 5 0.03 3.45 0.36 0.13 1.57 21.0 26.54 6 0.03 3.26 0.35 0.13 2.48 21.0 27.25 7 0.03 2.98 0.32 0.10 1.84 21.1 26.37 8 0.02 2.98 0.36 0.13 1.67 20.9 26.06 9 0.03 1.76 0.41 0.03 1.58 20.9 24.71 10 0.03 0.95 0.48 0.06 1.87 20.9 24.29

‘Important’ sources

Minimum reduction in PM Minimum reduction in PM10

10 needed

needed

g g Annual mean PM

Annual mean PM10

10 objective (40

• bjective (40 µ

µg m g m-3

• 3) -

) - ! !met met

g 24-hour objective (50 µ

µg m g m-3

• 3 - 35 exceedences)

− conservative surrogate statistics indicate annual mean > 28 µg m-3 possible exceedence − ‘best fit’ statistics indicate annual mean > 30 µg m-3 exceedence

g Need to reduce sources by 1 to 2 µ

µg m g m-3

• 3

g Target improvements on foundry operations?

Summary Summary

g g Need to understand the contribution from

Need to understand the contribution from

− background − other sources

g g Can then target appropriate sources

Can then target appropriate sources

g g Probably will need some more modelling and

Probably will need some more modelling and scenario testing scenario testing

g g Care with objectives with high percentiles

Care with objectives with high percentiles