2026-10-12T03:53:32Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33978195 2026-09-24T00:09:45Z category_146 category_21 category_272 category_39 category_734 category_106 portal_63 item_type_3 month_year_09_2026

How Many PM_2.5 Sensors are Needed to Estimate Daily and Annual Deployed-Network Mean Concentrations in City-Scale Sensor Networks? Trailokaya Raj Bajgain (25100187) Zachary D. Calhoun (25100190) Sachchida Nand Tripathi (5106068) Abdus Salam (1918387) Vaishali Jain (9718333) Sandeep Madhwal (25100193) Shahid Uz Zaman (10918790) Shatabdi Roy (25100196) Michael Bergin (5026979) David Carlson (688760) Space Science Biotechnology Environmental Sciences not elsewhere classified Ecology Biological Sciences not elsewhere classified Science Policy three finite networks tail performance must deploy dense networks density required depends 8 %, whereas 9 %, demonstrating scale sensor networks network mean concentrations approximately 10 well annual mean 61 annual mean 55 period mean 10 9 %, 5 typical daily means period mdape ranged 5 </ sub network mean network daily mean estimates target means single sensor scale gradients 8 %. estimate daily annual deployed 6 %, n </ typical errors subnetwork size replacement draws population exposure monitoring objective many pm local variability india (< exceedance classification estimating neighborhood enumerated exactly cost pm corresponding deployed corresponding 95th considered together bangladesh (< Low-cost PM_2.5 sensors make it possible to deploy dense networks, but the density required depends on the monitoring objective and local variability. We examined how well smaller subnetworks reproduce deployed-network daily and annual or study-period mean concentrations using data from Dhaka, Bangladesh (<i>N</i> = 35; annual mean 55.15 μg/m³), Lucknow, India (<i>N</i> = 71; annual mean 61.80 μg/m³), and the nine-month Open Air Chicago network (<i>N</i> = 277; period mean 10.24 μg/m³). For each subnetwork size <i>n</i> ≥ 2, mean estimates from 10,000 simple-random-without-replacement draws were compared with the corresponding deployed-network mean; the <i>n</i> = 1 errors were enumerated exactly. At <i>n</i> = 10, the period median absolute percentage error (MdAPE) was 3.9%, 5.6%, and 1.9% in Dhaka, Lucknow, and Chicago, respectively, and the across-day median daily MdAPE ranged from 2.6% to 5.8%. For a single sensor, the period MdAPE ranged from 5.2% to 17.8%, whereas the corresponding 95th-percentile absolute percentage error (APE) ranged from 18.5% to 59.9%, demonstrating that typical and tail performance must be considered together. Within these three finite networks, <i>n</i> = 10 reproduced the target means with typical errors below 6%, although upper-tail performance remained objective- and city-specific. Estimating neighborhood-scale gradients, hotspots, exceedance classification, and population exposure, however, requires network designs tailored to those objectives. Overall, approximately 10 well-maintained, spatially distributed sensors recover annual and typical daily means with an MdAPE below 10%. 2026-09-23T00:00:00Z Dataset Dataset 10.1021/acsestair.6c00035.s001 https://figshare.com/articles/dataset/How_Many_PM_sub_2_5_sub_Sensors_are_Needed_to_Estimate_Daily_and_Annual_Deployed-Network_Mean_Concentrations_in_City-Scale_Sensor_Networks_/33978195 CC BY-NC 4.0