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Building and environmental factors that influence bacterial and fungal loading on air conditioning cooling coils
Author(s) -
Bakker A.,
Siegel J. A.,
Mendell M. J.,
Peccia J.
Publication year - 2018
Publication title -
indoor air
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.387
H-Index - 99
eISSN - 1600-0668
pISSN - 0905-6947
DOI - 10.1111/ina.12474
Subject(s) - air conditioning , environmental science , bioaerosol , subtropics , humid subtropical climate , interquartile range , dew point , spore , mediterranean climate , bacteria , atmospheric sciences , biology , ecology , botany , meteorology , aerosol , medicine , geography , physics , surgery , genetics , pathology , thermodynamics
We investigated bacterial and fungal concentrations on cooling coils of commercial AC units and quantified associations between microbial loads and AC unit or building operational parameters. A field campaign was conducted to sample 25 AC units in the humid, subtropical climate of Southern CT , USA and 15 AC units in the hot‐summer Mediterranean climate of Sacramento, CA , USA . Median concentrations (with interquartile range) of bacteria and fungi on the cooling coils were 1.2 × 10 7 (5.1 × 10 6 ‐3.9 × 10 7 ) cells/m 2 and 7.6 × 10 5 (5.6 × 10 4 ‐4.4 × 10 6 ) spore equivalents (SE)/m 2 , respectively. Concentrations varied among units with median unit concentrations ranging three orders of magnitude for bacteria and seven orders of magnitude for fungi. Controlled comparisons and multivariable regressions indicate that dominant factors associated with AC coil loading include the nominal efficiency of upstream filters ( P = .008 for bacteria and P < .001 for fungi) and coil moisture, which was reflected in fungal loading differences between top and bottom halves of the AC coils in Southern CT ( P = .05) and the dew points of the two climates considered ( P = .04). Environmental and building characteristics explained 42% ( P < .001) of bacterial concentration variability and 66% ( P < .001) of fungal concentration variability among samples.