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<p>Air quality sensor (AQS) networks are useful for mapping PM<span class="inline-formula">_2.5</span> (particles with a diameter of 2.5 <span class="inline-formula">µm</span> or smaller) in urban environments, but quantitative assessment of the observed spatial and temporal variation is currently underdeveloped. This study introduces a new metric – the concentration similarity index (CSI) – to facilitate a quantitative and time-averaged comparison of the concentration–time profiles of PM<span class="inline-formula">_2.5</span> measured by each sensor within an air quality sensor network. Following development on a dataset with minimal unexplained variation and robust tests, the CSI function is used to represent an unbiased and fair depiction of the air quality variation within an area covered by a monitoring network. The measurement data is used to derive a CSI value for every combination of sensor pairs in the network, yielding valuable information on spatial variation in PM<span class="inline-formula">_2.5</span>. This new method is applied to two separate AQS networks, in Dungarvan and in the city of Cork, Ireland. In Dungarvan there was a lower mean CSI value (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M7" display="inline" overflow="scroll" dspmath="mathml"><mrow><msub><mover accent="true"><mi>x</mi><mo mathvariant="normal">‾</mo></mover><mtext>CSI, Dungarvan</mtext></msub><mo>=</mo><mn mathvariant="normal">0.61</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="100pt" height="15pt" class="svg-formula" dspmath="mathimg" md5hash="06a5bd71d271c136339c518548c33520"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="amt-17-5129-2024-ie00001.svg" width="100pt" height="15pt" src="amt-17-5129-2024-ie00001.png"/></svg:svg></span></span>, <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M8" display="inline" overflow="scroll" dspmath="mathml"><mrow><msub><mover accent="true"><mi>x</mi><mo mathvariant="normal">‾</mo></mover><mtext>CSI, Cork</mtext></msub><mo>=</mo><mn mathvariant="normal">0.71</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="78pt" height="15pt" class="svg-formula" dspmath="mathimg" md5hash="1be54eb2b0a7895dd9ce8e893f2d3802"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="amt-17-5129-2024-ie00002.svg" width="78pt" height="15pt" src="amt-17-5129-2024-ie00002.png"/></svg:svg></span></span>), indicating lower overall similarity between locations in the network. In both networks, the average diurnal plots for each sensor exhibit an evening peak in PM<span class="inline-formula">_2.5</span> concentration due to emissions from residential solid-fuel burning; however, there is considerable variation in the size of this peak. Clustering techniques applied to the CSI matrices identify two different location types in each network; locations in central or residential areas that experience more pollution from solid-fuel burning and locations on the edge of the urban areas that experience cleaner air. The difference in mean PM<span class="inline-formula">_2.5</span> between these two location types was 6 <span class="inline-formula">µg m⁻³</span> in Dungarvan and 2 <span class="inline-formula">µg m⁻³</span> in Cork. Furthermore, the examination of winter and summer months (January and May) indicates that higher PM<span class="inline-formula">_2.5</span> levels during periods of increased residential solid-fuel burning act as a major driver for greater differences (lower similarity indices) between locations in both networks, with differences in mean seasonal CSI values exceeding 0.25 and differences in mean seasonal PM<span class="inline-formula">_2.5</span> exceeding 7 <span class="inline-formula">µg m⁻³</span>. These findings underscore the importance of including wintertime PM data in analyses, as the differences between locations is enhanced during periods when solid-fuel burning activities are at a peak. Additionally, the CSI method facilitates the assessment of the representativeness of the PM<span class="inline-formula">_2.5</span> measured at regulatory air quality monitoring locations with respect to population exposure, showing here that location type is more important than physical proximity in terms of similarity and spatial representativeness assessments. Applying the CSI in this manner can allow for the placement of monitoring infrastructure to be optimised. The results indicate that the population exposure to PM<span class="inline-formula">_2.5</span> in Dungarvan is moderately represented (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M18" display="inline" overflow="scroll" dspmath="mathml"><mrow><msub><mover accent="true"><mi>x</mi><mo mathvariant="normal">‾</mo></mover><mtext>CSI</mtext></msub><mo>=</mo><mn mathvariant="normal">0.63</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="56pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="6d586fe7a5257920de1922ac86a42570"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="amt-17-5129-2024-ie00003.svg" width="56pt" height="14pt" src="amt-17-5129-2024-ie00003.png"/></svg:svg></span></span>) by the current regulatory monitoring location, and the regulatory monitoring location assessed in Cork represented the city-wide PM<span class="inline-formula">_2.5</span> levels well (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M20" display="inline" overflow="scroll" dspmath="mathml"><mrow><msub><mover accent="true"><mi>x</mi><mo mathvariant="normal">‾</mo></mover><mtext>CSI</mtext></msub><mo>=</mo><mn mathvariant="normal">0.76</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="56pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="8372590b02a1c88b7742e987ec98d37d"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="amt-17-5129-2024-ie00004.svg" width="56pt" height="14pt" src="amt-17-5129-2024-ie00004.png"/></svg:svg></span></span>).</p>