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A prediction of dust mite populations in different categories of housing quality in Auckland, New Zealand

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Summary

Background

New Zealand has one of the highest asthma rates in the world, frequently linked to the quality of housing. A confirmed causal factor for asthma is the dust mite allergen, with environmental factors such as temperature and relative humidity (RH) linked to the proliferation of dust mites. Whilst significant research has been undertaken into the habitat and ecology of dust mites, little research has been conducted comparing different categories of housing quality in relation to dust mites.

Methods

A dust mite population prediction algorithm (POPMITE) was used to undertake a comparative analysis of the potential of three housing types (old, newly constructed and newly constructed green certified) to harbour dust mites.

Results

The results determined no statistically significant difference between the quality categories, and the hypothesis that newly constructed, more insulated dwellings would have lower predicted dust mite populations was rejected. Instead, the newly constructed dwellings exhibited a wide range of predicted dust mite populations indicating a potential sensitivity to occupant behaviour in new housing stock which was not experienced in the older vintage dwellings.

Conclusions

Occupant behaviour is suggested as an influencing factor with a call for policy makers to consider real-time feedback loops to dwelling occupants, in conjunction with structural interventions, to improve the health of the interior environment.

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Notes

  1. Human Participants Ethics Committee approval was granted prior to installation of the data loggers.

Abbreviations

6HS:

6‑Homestar

AH:

Absolute humidity

CEH:

Critical equilibrium humidity

DP:

Dermatophagoides pteronyssinus

IEA:

International Energy Agency

LPWAN:

Low-powered wider area network connection

MOTAT:

Museum of Transport and Technology

NEW:

New building stock

NZBC:

New Zealand Building Code

OLD:

Old building stock

POPMITE:

A dust mite population prediction algorithm

RH:

Relative humidity

WOF:

Warrant of fitness

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Acknowledgements

This study was supported by funding from the Building Research Levy.

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Authors and Affiliations

  1. Department of Property, University of Auckland Business School, Auckland, New Zealand

    Rochelle Ade & Michael Rehm

Authors
  1. Rochelle Ade
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  2. Michael Rehm
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Corresponding author

Correspondence to Rochelle Ade.

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Conflict of interest

R. Ade is a Homestar Assessor and has worked as a Homestar Assessor for the Building Excellence Group. R. Ade also has an interest in Tether. M. Rehm declares that he has no competing interests.

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Ade, R., Rehm, M. A prediction of dust mite populations in different categories of housing quality in Auckland, New Zealand. Allergo J Int 29, 187–198 (2020). https://doi.org/10.1007/s40629-020-00130-w

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  • DOI: https://doi.org/10.1007/s40629-020-00130-w

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