A New Combined Air Quality and Heat Index in Relation to Mortality in Monterrey, Mexico

Fever, Shayna K.; Kahl, Jonathan D. W.; Kalkbrenner, Amy E.; Bretón, Rosa María Cerón; Bretón, Julia Griselda Cerón

doi:10.3390/ijerph19063299

Cited by 4 publications

(1 citation statement)

References 40 publications

(45 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yet, there is no consistent method to communicate the combined risk. Fever et al [30] unified the HI and AQI for PM 2.5 and ozone in a combined index using several different models. They found that improved mortality predictions were possible with their combined indices in Monterrey, Mexico, but cautioned that their results might not be generalizable to regions with different air pollutant and weather patterns.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Correction of PurpleAir Temperature and Relative Humidity Measurements

Couzo,

Valencia,

Gittis

2024

Atmosphere

View full text Add to dashboard Cite

The PurpleAir PA-II sensor provides low-cost in situ measurements of meteorological variables including temperature and relative humidity (RH), as well as fine particulate matter (PM2.5) in real time. The sensors have been used in several studies investigating intracity differences in temperature and PM2.5. While the adoption and use of low-cost sensors has many benefits, care must be taken to ensure proper calibration and testing. This is true not only for PM2.5 measurements but also for temperature and RH given the synergistic health impacts from extreme heat and air pollution exposure. Here, we compare continuous temperature and RH measurements from a PA-II sensor to measurements from a Campbell Scientific 107 temperature probe and Vaisala HMP45C RH probe. All three instruments were co-located from December 2021 to June 2023 in Asheville, North Carolina. We found that the PA-II has an overall high temperature bias of 2.6 °C and root mean square error (RMSE) of 2.8 °C. Applying a linear regression correction reduces RMSE to 1.0 °C, while applying the constant 4.4 °C correction suggested by PurpleAir reduces RMSE to only 2.2 °C. Our PA-II RH measurements have a low bias of −17.4% and uncorrected RMSE of 18.5%. A linear regression correction improves the RH RMSE to 4.5%. Applying the constant 4% RH correction suggested by PurpleAir reduces RMSE to only 14.8%. We present new correction factors that differ from those suggested by PurpleAir, which overcorrect the high temperature bias and undercorrect the low RH bias. We also show that our correction factors improve estimates of dewpoint temperature (RMSE of 0.6 °C and 0.9 °C) compared to the corrections suggested by PurpleAir.

show abstract

Section: Introductionmentioning

confidence: 99%