CLIMATE DOES MATTER- UNCERTAINTY IN AIR QUALITY NETWORKS
Air quality monitoring stations (termed Automatic Urban and Rural Networks- AURNs in the UK) us either certifi ed reference or equivalent instrumentation. They are trusted for their accuracy and uncertainty to measure air pollutants, monitoring compliance with the legislated Limit Values for gases and particles. Unfortunately, we cannot afford to deploy the number of AURNs needed to properly map our urban air pollution. To remedy this situation, we now have low cost air quality (AQ) networks, comprising from ten to hundreds of sensor systems throughout an urban area, providing near-real time pollution maps. Their cost is about 5% the cost of an AURN, but ensuring their calibration is a serious problem and is the focus of much current research, with Machine Learning (ML)1,2 baseline correction3
, and reference co-location4 being three popular calibration methods.
One problem without a solution is the simple fact that most low cost AQ networks are built and calibrated in either Europe or North America, in temperate climates. They are then deployed globally, especially in the most polluted cities in countries including India, Pakistan, China and Mexico; the climates are very different from where they were calibrated. Does that matter? Yes, climate matters. Temperature, humidity and pressure change continually, with different environmental profi les in different climates. This is important because low cost sensors also respond to temperature, humidity and pressure, leading to potentially signifi cant errors in the reported pollutant concentrations.
Environmental Effects
on Low Cost Sensors Four technologies dominate sensors for low cost AQ sensor systems: electrochemical gas sensors and metal oxide chemresistors for gases, laser scattering for particulate matter (PM) and non-dispersive infrared (NDIR) for CO2
How do they each respond to environmental conditions?
Electrochemical cells generate a current as they either oxidise or reduce the target gas. Their temperature dependence is provided by sensor manufacturers but rapid temperature changes cause transients that can take hours to restabilise. Hot temperatures (>35°C) create high background currents which is very troublesome when measuring ppb concentrations. Importantly, humidity affects the sensor electrochemistry and rapid humidity changes generate excess current from the sensor, like a supercapacitor, resulting in large current transients6. These transients are diffi cult to model and are the largest source of data variance when using AQ sensor systems in a dynamic climate. Atmospheric pressure changes are usually slow and have negligible effect, but sudden air fl ow changes (e.g. ventilation systems) can cause transients which recover in a few minutes.
Metal oxide (MOx) sensors change resistance as the target gas interacts with the metal oxide surface. MOx sensors are relatively free of ambient temperature effects because they should operate at fi xed high temperatures, but humidity has a signifi cant effect by changing the surface chemistry, especially with n-type MOx sensors such as SnO2
based sensors. Applying RH correction
algorithms are only partially successful because the sensors drift with time, away from the correcting algorithm. MOx sensors are used in lowest cost AQ networks, but due to their humidity dependence and drift, are mostly avoided in the higher quality AQ sensor systems. Again, pressure has a small effect and can be easily corrected.
Dsa Hot, dry summer continental
Dwa Monsoon infl uenced hot summer humid continental
Dwb Cold moderate Salt Lake City Beijing Vladivaskof <0 <0 <0 >10 >10 >10 >22 >22 >22 Dry winter Class
TROPICAL Af
Defi nition Cities (Potential fi eld test site) Tropical rainforest
Am Tropical monsoon As Aw
Dry savannah Wet savannah
DRY BSh Hot semi-arid BSk
BWh Hot desert and other gases. BWk Cold desert
TEMPERATE Cfa Cfb Cfc
Humid sub-tropical Temperate oceanic Polar island
Csa Hot summer Mediterranean
Csb Warm summer Mediterranean
Cwa Dry winter humid sub- tropical
Cwb Subtropical Highland
Hot summer humid continental
Kuala Lumpur, Singapore Miami, Jakarta
Mombasa, Ghana, Hawaii
Lagos Mumbai Rio West Key
Cold semi-arid
Honolulu, Diamond Bar >0 Denver, Casper
Las Vegas, Phoenix, Karaachi
Xinjang, Damascus
<0 >0
<0
Toulouse, Milan, Bologna >0 London, Paris, Karlsruhe >0 Rejkevik
LA, Barcelona, Rome
San Francisco, Pacifi c coast
Hong Kong Nairobi, Mexico City
CONTINENTAL (NORTHERN HEMISPHERE ONLY) Dfa
Dfb Warm summer continental Helsinki, Oslo Boston, Toronto, Minn. >0 >0 >0 >0 <0 <0
0 to 18 >22 0 to 18
0 to 18 >22 0 to 18 0 to 18 >22 0 to 18 >10 >10 >22 <22
Coldest Month °C
Average Month °C
>18 >18 >18 >18
>1 month max temp °C
Rainfall
Winter: Summer
>60 mm rain aseasonal
<60, mm rain Dry after 21/12 <60 mm rain Dry season >60 mm rain
>18
<18 <18
Little precip. Steppe Little precip. steppe Little precip. Very dry
Little precip. Very dry No dry season dry: wet <22 No dry season same <30 <22 <30 wet: dry wet: dry dry: wet dry: wet No dry season same
<22 No dry season
<30 same 3x dry: wet
IET SEPTEMBER 2022
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