The Relationship Between Air Pollution and All-Cause Mortality in Singapore

Circular economy strategies in the ASEAN region: A comparative study

Circular Economy (CE) is a sustainable development paradigm that promotes resource efficiency, closed-loop systems, and waste reduction to minimize environmental impacts while fostering economic growth; its popularity is rising at a global scale since the negative effects of linear consumption patterns become more apparent. In this direction, the ASEAN (Association of South East Asian Nations) countries have shown a rising interest in CE due to the region's rapid economic growth and urbanization led to increasing resource consumption and waste generation, which makes CE imperative to safeguard their natural resources and ecosystems. The methodology assessed and compared CE policy documents and academic sources, focusing on excellence and expected impacts, excluding obsolete policies. As the foremost finding, this work provides a comprehensive assessment of the CE strategies comparing the ten ASEAN countries for understanding the current direction of circularity across the region, which is insufficient, although the need for a CE is understood and numerous policy strategies are currently in the work or pending to be approved; Vietnam is the most promising nation for CE implementation. Brunei, Laos, and Myanmar are the most stagnant, while the rest of the countries are progressing adequately. First, this paper introduces the most critical environmental issues across the ASEAN region and briefly describes the concept of CE. Secondly, it assesses the most up-to-date and remarkable CE policies of each nation. Thirdly, it discusses how CE can address their challenges to be catalyzed into opportunities, comparing the ten states considering their CE advancements. This work will be interesting for foreign investors, the general public, Academia, and policymakers.

Leveraging open data to reconstruct the Singapore Housing Index and other building-level markers of socioeconomic status for health services research

BACKGROUND

Socioeconomic status (SES) is an important determinant of health, and SES data is an important confounder to control for in epidemiology and health services research. Individual level SES measures are cumbersome to collect and susceptible to biases, while area level SES measures may have insufficient granularity. The 'Singapore Housing Index' (SHI) is a validated, building level SES measure that bridges individual and area level measures. However, determination of the SHI has previously required periodic data purchase and manual parsing. In this study, we describe a means of SHI determination for public housing buildings with open government data, and validate this against the previous SHI determination method.

METHODS

Government open data sources (e.g.

DATA

gov.sg, Singapore Land Authority OneMAP API, Urban Redevelopment Authority API) were queried using custom Python scripts. Data on residential public housing block address and composition from the HDB Property Information dataset (data.gov.sg) was matched to postal code and geographical coordinates via OneMAP API calls. The SHI was calculated from open data, and compared to the original SHI dataset that was curated from non-open data sources in 2018.

RESULTS

Ten thousand seventy-seven unique residential buildings were identified from open data. OneMAP API calls generated valid geographical coordinates for all (100%) buildings, and valid postal code for 10,012 (99.36%) buildings. There was an overlap of 10,011 buildings between the open dataset and the original SHI dataset. Intraclass correlation coefficient was 0.999 for the two sources of SHI, indicating almost perfect agreement. A Bland-Altman plot analysis identified a small number of outliers, and this revealed 5 properties that had an incorrect SHI assigned by the original dataset. Information on recently transacted property prices was also obtained for 8599 (85.3%) of buildings.

CONCLUSION

SHI, a useful tool for health services research, can be accurately reconstructed using open datasets at no cost. This method is a convenient means for future researchers to obtain updated building-level markers of socioeconomic status for policy and research.

Biomass burning-derived airborne particulate matter in Southeast Asia: A critical review

Smoke haze episodes, resulting from uncontrolled biomass burning (BB) including forest and peat fires, continue to occur in Southeast Asia (SEA), affecting air quality, atmospheric visibility, climate, ecosystems, hydrologic cycle and human health. The pollutant of major concern in smoke haze is airborne particulate matter (PM). A number of fundamental laboratory, field and modeling studies have been conducted in SEA from 2010 to 2020 to investigate potential environmental and health impacts of BB-induced PM. The goal of this review is to bring together the most recent developments in our understanding of various aspects of BB-derived PM based on 127 research articles published from 2010 to 2020, which have not been conveyed in previous reviews. Specifically, this paper discusses the physical, chemical, toxicological and radiative properties of BB-derived PM. It also provides insights into the environmental and health impacts of BB-derived PM, summarizes the approaches taken to do the source apportionment of PM during BB events and discusses the mitigation of exposure to BB-derived PM. Suggestions for future research priorities are outlined. Policies needed to prevent future BB events in the SEA region are highlighted.

The Complexity of Space Utilization and Environmental Pollution Control in the Main Corridor of Makassar City, South Sulawesi, Indonesia

Population mobility, increasing demand for transportation, and the complexity of land use have an impact on environmental quality degradation and air quality pollution. This study aims to analyze (1) the effect of population mobility, increased traffic volume, and land use change on air quality pollution, (2) direct and indirect effects of urban activities, transportation systems, and movement patterns on environmental quality degradation and air pollution index, and (3) air pollution strategy and sustainable urban environmental management. The research method used is a sequential explanation design. Data were obtained through observation, surveys, in-depth interviews, and documentation. The results of the study illustrate that the business center and Daya terminal with a value of 0.18 µgram/m3 is polluted, the power plant and Sermani industrial area with a value of 0.16 µgram/m3 is polluted, the Makassar industrial area with a value of 0.23 is heavily polluted, and the Hasanuddin International Airport area with a value of 0.04 µgram/m3 is not polluted. Population mobility, traffic volume, and land use changes have a significant effect on environmental quality degradation, with a determination coefficient of 94.1%. The direct effect of decreasing environmental quality on the air pollution index is 66.09%. This study recommends transportation management on the main road corridor of Makassar City, which is environmentally friendly with regard to sustainable environmental management.

Leveraging Machine Learning Techniques and Engineering of Multi-Nature Features for National Daily Regional Ambulance Demand Prediction

The accurate prediction of ambulance demand provides great value to emergency service providers and people living within a city. It supports the rational and dynamic allocation of ambulances and hospital staffing, and ensures patients have timely access to such resources. However, this task has been challenging due to complex multi-nature dependencies and nonlinear dynamics within ambulance demand, such as spatial characteristics involving the region of the city at which the demand is estimated, short and long-term historical demands, as well as the demographics of a region. Machine learning techniques are thus useful to quantify these characteristics of ambulance demand. However, there is generally a lack of studies that use machine learning tools for a comprehensive modeling of the important demand dependencies to predict ambulance demands. In this paper, an original and novel approach that leverages machine learning tools and extraction of features based on the multi-nature insights of ambulance demands is proposed. We experimentally evaluate the performance of next-day demand prediction across several state-of-the-art machine learning techniques and ambulance demand prediction methods, using real-world ambulatory and demographical datasets obtained from Singapore. We also provide an analysis of this ambulatory dataset and demonstrate the accuracy in modeling dependencies of different natures using various machine learning techniques.

Air-Pollution Control in an Emergent Market: Does It Work? Evidence from Romania

Economic development in a national and international context must be based on a sustainability strategy established on the systemic interaction between the economic, sociocultural, and ecological environments. Today, the world is confronted by many challenges related to climate change and natural-resource flows, including waste streams resulting from economic activity. The need for national and European environmental standards and the work of an environment monitoring authority to reduce air pollution are highlighted by economic and industrial activities. Thus, our research focused on determining if emissions of sulfur dioxide (SO₂), nitrogen (NO₂), and particulate matter 10 (PM₁₀) are influenced by planned and unplanned inspections made by competent authorities from Romania. We built a regression model that estimates the influence of economic measures imposed by the authorities on reducing industrial air pollution. Preliminary results showed that the number of inspections negatively influences air pollution, indicating that national and local authorities in Romania are striving to maintain air quality and are conducting more inspections when air pollution is high.

Impact of Air Pollution and Trans-Boundary Haze on Nation-Wide Emergency Department Visits and Hospital Admissions in Singapore

Introduction: Air pollution is associated with adverse health outcomes. However, its impact on emergency health services is less well understood. We investigated the impact of air pollution on nation-wide emergency department (ED) visits and hospital admissions to public hospitals in Singapore. Materials and Methods: Anonymised administrative and clinical data of all ED visits to public hospitals in Singapore from January 2010 to December 2015 were retrieved and analysed. Primary and secondary outcomes were defined as ED visits and hospital admissions, respectively. Conditional Poisson regression was used to model the effect of Pollutant Standards Index (PSI) on each outcome. Both outcomes were stratified according to subgroups defined a priori based on age, diagnosis, gender, patient acuity and time of day. Results: There were 5,791,945 ED visits, of which 1,552,187 resulted in hospital admissions. No significant association between PSI and total ED visits (Relative risk [RR], 1.002; 99.2% confidence interval [CI], 0.995–1.008; P = 0.509) or hospital admissions (RR, 1.005; 99.2% CI, 0.996–1.014; P = 0.112) was found. However, for every 30-unit increase in PSI, significant increases in ED visits (RR, 1.023; 99.2% CI, 1.011–1.036; P = 1.24 × 10˗6) and hospital admissions (RR, 1.027; 99.2% CI, 1.010–1.043; P = 2.02 × 10˗5) for respiratory conditions were found. Conclusion: Increased PSI was not associated with increase in total ED visits and hospital admissions, but was associated with increased ED visits and hospital admissions for respiratory conditions in Singapore. Key words: Epidemiology, Healthcare utilisation, PSI, Public health, Time series