A Statistical Analysis of the Relationship between Brown Haze and Surface Air Pollution Levels on Respiratory Hospital Admissions in Auckland, New Zealand

Abstract

Eleven years of hospital admissions data for Auckland, New Zealand for respiratory conditions are analyzed using a Poisson regression modelling approach, incorporating a spline function to represent time, based on a detailed record of haze events and surface air pollution levels over an eleven-year period, taking into account the daily average temperature and humidity, the day of the week, holidays and trends over time. NO 2 was the only pollutant to show a statistically significant increase (p = 0.009) on the day of the haze event for the general population. Ambient concentrations of CO, NO and NO 2 were significantly associated with admissions with an 11-day lag period for the 0-14 year age group and a 5-7 day lag period for the 65+ year age group. A 3-day lag period was found for the 15-64 year age group for CO, NO and PM 10 . Finally, the incidence of brown haze was linked to significant increases in hospital admissions. A lag period of 5 days was recorded between haze and subsequent increases in admissions for the 0-14 year age group and the 65+ group and an 11-day lag for the 15-64 year age group. The results provide the first statistical link between Auckland brown haze events, surface air pollution and respiratory health. Medical institutions and practitioners could benefit from improved capacity to predict Auckland's brown haze events in order prepare for the likely increases in respiratory admissions over the days ahead.

Ambient particulate matter and biomass burning: an ecological time series study of respiratory and cardiovascular hospital visits in northern Thailand

“…In our study, we used the 90th and 95th percentiles of PM 10 concentrations (i.e., 87.1 μg/m 3 and 109.6 μg/m 3 , respectively) to identify days of biomass burning exposure, which is slightly lower than the 99 percentile employed by Morgan et al [35]. A range of other approaches also have been employed to identify days with exposure to burning, or haze, including a doubling of total suspended particulates (mean = 56.9 μg/m 3 [49]), the extent of discoloration in the sky [36], a threshold of 80 μg/m 3 to indicate 'unhealthy' levels [50], and exposure in the month of March [46]. Other studies of exposure to fires have employed software to track polluted air mass trajectories based on meteorological data (e.g., [51]).…”

“…For cardiovascular outcomes, short-term exposure to PM has been associated with such changes as reduced heart rate variability, increased diastolic blood pressure, and enhanced arterial vasoconstriction and blood coagulation, all of which may contribute to acute events [32]. Larger risks on the same day of PM 10 exposure compared to lagged estimates have also been identified in urban contexts, including all hospital admissions in 218 Chinese cities [33], COPD admissions in Beijing [22], and also specifically with biomass burning: bushfire and respiratory admissions in Australia [34,35], and haze and respiratory admissions in New Zealand (ages 15-64 years only [36]). Our results with CBVD visits, but not IHD, conflict with findings from Morgan et al [35], where a relationship with cardiovascular admissions was not identified using any lag.…”

Classification of Potential Risk Factors through HIRARC Method in Assessing Indoor Environment of Museums

“…The population in the urban area is approximately over 100,000 people, whereas the population can vary from 10,000 to 100,000 people for suburban area, and population in rural area is usually under 10,000 people. Previous studies conducted by several researchers stressed that building's age [27][28][29] and location [34][35][36][37][38][39][40][41][42][43][44] are important to be considered when assessing building characteristics. The summary of the matrix score criteria (Criteria 4) classifying the museum's characteristics into different risk possibilities namely low potential risk, medium potential risk, high potential risk, or extreme potential risk, as shown in Table 14.…”

Towards the Integrated Study of Urban Climate, Air Pollution, and Public Health

“…reported, in Kuopio (Finland) [13] and Auckland (New Zealand) where Dirks et al [14] demonstrate the relation between daily mortality and local traffic-related air pollutants.…”