Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Conditional extraction of air-pollutant source signals from air-quality monitoring
AU - Malby, A. R.
AU - Whyatt, Duncan
AU - Timmis, Roger
PY - 2013/8
Y1 - 2013/8
N2 - Ambient air-quality data contain information about air-pollution sources that is currently under-exploited. This information could be used to assess trends in the emissions performance of specific sources, and to check at an early stage if policies or controls to reduce air-quality impacts from particular sources are working. Previous techniques for extracting such information have tended to adopt complex analyses and to rely on data from monitoring networks with many sites, thus limiting their applicability to non-specialist users and to networks with few sites. This paper describes simple techniques for ‘conditionally’ selecting data from one or two monitors, and for analysing and interpreting concentrations in terms of source performance or policy progress. Our techniques minimise the effects of variations in meteorology and source activity, so that the selected data give a more consistent indication of individual source performance. We demonstrate our techniques with a case study, in which we track the source performance of road traffic on the M4 motorway in London and show how impacts per vehicle have changed over time under different conditions of traffic flow and fleet composition.
AB - Ambient air-quality data contain information about air-pollution sources that is currently under-exploited. This information could be used to assess trends in the emissions performance of specific sources, and to check at an early stage if policies or controls to reduce air-quality impacts from particular sources are working. Previous techniques for extracting such information have tended to adopt complex analyses and to rely on data from monitoring networks with many sites, thus limiting their applicability to non-specialist users and to networks with few sites. This paper describes simple techniques for ‘conditionally’ selecting data from one or two monitors, and for analysing and interpreting concentrations in terms of source performance or policy progress. Our techniques minimise the effects of variations in meteorology and source activity, so that the selected data give a more consistent indication of individual source performance. We demonstrate our techniques with a case study, in which we track the source performance of road traffic on the M4 motorway in London and show how impacts per vehicle have changed over time under different conditions of traffic flow and fleet composition.
KW - Conditional Analysis
KW - Signal Detection
KW - Source Performance
KW - Ambient Trends
KW - Vehicle Emissions
UR - http://www.scopus.com/inward/record.url?scp=84876715081&partnerID=8YFLogxK
U2 - 10.1016/j.atmosenv.2013.03.028
DO - 10.1016/j.atmosenv.2013.03.028
M3 - Journal article
AN - SCOPUS:84876715081
VL - 74
SP - 112
EP - 122
JO - Atmospheric Environment
JF - Atmospheric Environment
SN - 1352-2310
ER -