The Effects of Air Quality on Hospital Admissions for Chronic Respiratory Diseases in Petaling Jaya, Malaysia, 2013–2015

Assessing the impacts of climate variables on long-term air quality trends in Peninsular Malaysia

Climate change is thought to influence the composition of atmospheric air, but little is known about the direct relationship between these variables, especially in a hot tropical climate like that of Malaysia. This work summarizes and analyzes the climate state and air quality of Peninsular Malaysia based on selected ground-based observations of the temperature, precipitation, relative humidity, wind speed, wind direction and concentrations of PM10, O3, CO, NO2, and SO2 over the last 20 years (2000-2019). The relationship between the climate state and air quality is analyzed using the Pearson correlation and canonical correlation analysis (CCA) methods is employed to predict the degree of change in the future air quality under different warming scenarios. It is found that the Peninsular Malaysia mainly experienced strong precipitation in the central and mountainous regions, while air pollutants are primarily concentrated in densely populated areas. Throughout the period of study (interannual, monthly, and diurnal time series analyses), Peninsular Malaysia became warmer and drier, with a significant increase in temperature (+4.2 %), decrease in the relative humidity (-4.5 %), and greater fluctuation in precipitation amount. The pollution conditions have worsened; there has been an increase in the PM10 (+16.4 %), O3 (+39.5 %), and NO2 (+2.1 %) concentration over the last 20 years. However, the amount of SO2 (-53.6 %) and CO (-20.6 %) decreased significantly. The analysis of the monthly variation shows a strong bimodality of the PM10 and O3 concentrations that corresponds to the monsoon transition. Intensive diurnal fluctuations and correlations are observed for all the variables in this study. According to the CCA, the air quality factors are strongly correlated with meteorological factors; in particular, the CO, O3, and PM10 concentrations interact strongly with the air temperature. These findings show that the future air quality in Peninsular Malaysia has high possibility to deteriorate under warming condition.

Perceived impacts of Urban Heat Island phenomenon in a tropical metropolitan city: Perspectives from stakeholder dialogue sessions

The stakeholders' perceptions on the impacts of Urban Heat Island (UHI) are critical for reducing exposure and influencing their response to interventions that are aimed at encouraging a behaviour change. A proper understanding of the UHI impacts on the society, economy and environment is deemed an essential motivating factor for the stakeholders to work towards UHI mitigations in the local context. This study adopted an inductive qualitative approach using Stakeholder Dialogue Sessions (SDSs) to assess the perceived impacts of UHI among various stakeholders, comprising policy makers, academicians, developers and Non-Governmental Organizations (NGO), in a tropical metropolitan city. The results revealed five themes such as deterioration of public health, acceleration of urban migration patterns and spending time in cooler areas, reduction of workers' productivity, increased energy consumption by the households and deterioration of environmental quality and natural resources that were categorized into social, economic and environmental impacts. Although most of the stakeholders were quite unfamiliar with the term UHI, they still display a good understanding of the potential impacts of UHI due to their posteriori knowledge and ability to rationalize the physical condition of the environment in which they live. The findings provide useful insights and valuable information to the local authorities to tailor necessary actions and educational campaigns to increase UHI awareness among the stakeholders. Being among the earlier studies to use a qualitative approach to attain the aforementioned objective, the findings are crucial to determine the level of understanding of the stakeholders on the impact of UHI. Through this study, the authors have highlighted the gaps and needs for knowledge improvements aimed at behaviour change among the stakeholders.

Unorganized Machines to Estimate the Number of Hospital Admissions Due to Respiratory Diseases Caused by PM10 Concentration

The particulate matter PM10 concentrations have been impacting hospital admissions due to respiratory diseases. The air pollution studies seek to understand how this pollutant affects the health system. Since prediction involves several variables, any disparity causes a disturbance in the overall system, increasing the difficulty of the models’ development. Due to the complex nonlinear behavior of the problem and their influencing factors, Artificial Neural Networks are attractive approaches for solving estimations problems. This paper explores two neural network architectures denoted unorganized machines: the echo state networks and the extreme learning machines. Beyond the standard forms, models variations are also proposed: the regularization parameter (RP) to increase the generalization capability, and the Volterra filter to explore nonlinear patterns of the hidden layers. To evaluate the proposed models’ performance for the hospital admissions estimation by respiratory diseases, three cities of São Paulo state, Brazil: Cubatão, Campinas and São Paulo, are investigated. Numerical results show the standard models’ superior performance for most scenarios. Nevertheless, considering divergent intensity in hospital admissions, the RP models present the best results in terms of data dispersion. Finally, an overall analysis highlights the models’ efficiency to assist the hospital admissions management during high air pollution episodes.