BACKGROUND
Associations between long-term exposure to ambient particulate matter (PM) and cardiovascular (CVD) mortality have been widely recognized. However, health effects of long-term exposure to constituents of PM on total CVD mortality have been explored in a single study only.


AIMS
The aim of this study was to examine the association of PM composition with cardiovascular mortality.


METHODS
We used data from 19 European ongoing cohorts within the framework of the ESCAPE (European Study of Cohorts for Air Pollution Effects) and TRANSPHORM (Transport related Air Pollution and Health impacts--Integrated Methodologies for Assessing Particulate Matter) projects. Residential annual average exposure to elemental constituents within particle matter smaller than 2.5 and 10 μm (PM2.5 and PM10) was estimated using Land Use Regression models. Eight elements representing major sources were selected a priori (copper, iron, potassium, nickel, sulfur, silicon, vanadium and zinc). Cohort-specific analyses were conducted using Cox proportional hazards models with a standardized protocol. Random-effects meta-analysis was used to calculate combined effect estimates.


RESULTS
The total population consisted of 322,291 participants, with 9545 CVD deaths. We found no statistically significant associations between any of the elemental constituents in PM2.5 or PM10 and CVD mortality in the pooled analysis. Most of the hazard ratios (HRs) were close to unity, e.g. for PM10 Fe the combined HR was 0.96 (0.84-1.09). Elevated combined HRs were found for PM2.5 Si (1.17, 95% CI: 0.93-1.47), and S in PM2.5 (1.08, 95% CI: 0.95-1.22) and PM10 (1.09, 95% CI: 0.90-1.32).


CONCLUSION
In a joint analysis of 19 European cohorts, we found no statistically significant association between long-term exposure to 8 elemental constituents of particles and total cardiovascular mortality.

Long-term exposure to elemental constituents of particulate matter and cardiovascular mortality in 19 European cohorts: Results from the ESCAPE and TRANSPHORM projects

Geographic information system (GIS) is designed to generate maps, manage spatial datasets, perform sophisticated “what if” spatial analyses, visualize multiple spatial datasets simultaneously, and solve location-based queries. The impact of big data is in every industry, including the GIS. The location-based big data also known as big spatial data has significant implications as it forces the industry to contemplate how to acquire and leverage spatial information. In this study, a comprehensive taxonomy is created to provide a better understanding of the uses of GIS and big spatial data. The taxonomy is made up of big data technologies, GIS data sources, tools, analytics, and applications. The authors look into the importance of big spatial data and its implications, review the data sources, and GIS analytics, applications, and GIS tools. Furthermore, in order to guide researchers interested in GIS, the challenges that require substantial research efforts are taken into account. Lastly, open issues in GIS that require further observation are summarized.

Geographic Information System and Big Spatial Data: A Review and Challenges

Spatial assessment of PM10 hotspots using Random Forest, K-Nearest Neighbour and Naïve Bayes

Volatile organic compounds and their contribution to ground-level ozone formation in a tropical urban environment.

COVID-19 is an infectious disease caused by a novel strain of Coronavirus. COVID-19 was declared a global pandemic on 11th March 2020, and it has impacted almost every aspect of human life. Education is crucial to individuals and society, and COVID-19 has affected educational systems worldwide. According to UNESCO monitoring, over 100 countries have implemented nationwide closures, impacting nearly 90% of the world's student population. In this chapter, the authors discuss how ICT is helping the community during COVID-19 and how it is catering to the young population's education needs. The demographics of COVID-19, along with an aging population, are explored. The authors then focus on the education sector and the challenges for education from school to universities. Furthermore, they look into how ICT facilitates education in the COVID-19 pandemic and the issues and challenges ICT faces in implementing online education. In the end, they discuss the role of ICT in developing communities and the future directions of online education for the development of smart communities.

Role of ICT for Community in Education During COVID-19

Air pollution is strongly tied to climate change. Industrialization and fossil fuel combustion are the main contributors leading to climate change, also being significant sources of air pollution. Malaysia is a developing country with a focus on industrialization. The preference of using private cars is a common practice in Malaysia, resulting in the after-effects of haze and transboundary air pollution. Hence, air pollution has become a severe issue in Malaysia in recent times. Exposure to air pollutants such as ozone and airborne particles is associated with increases in hospital admissions and mortality. For the past few years, the focus of the research is moving towards air quality and the impacts of air pollution on health in Malaysia. In this study, we establish the definition of air pollution, the motivation to study it, and its impacts and sources of air pollution and climate change. We discuss the air quality monitoring system in Malaysia and compare Malaysian ambient air quality standards with global standards. We also look comprehensively on the health impacts of air pollution globally and in the Malaysian context. We discuss where the health impact studies in Malaysia are lacking and what are the gaps in the research. The role of the Malaysian government concerning air pollution and its impacts is discussed. Lastly, we look into the future work and research opportunities with a focus on engineering, estimation, predictive models and lack of research projects.

Air pollution and its health impacts in Malaysia: a review

Dear Editor: This manuscript is already online in techrxiv (https://doi.org/10.36227/techrxiv.12376427.v1), and the co-authors are not correctly associated with the published preprint so we are submitting this again by associating the co-authors. We have also improved the similarity report, the similarity index is 11% of this submission. Thank you.Abstract: Air pollution is one of the significant causes
of mortality and morbidity every year. In recent years, many researchers have
focused their attention on the associations of air pollution and health. These
studies used two types of data in their studies, i.e., air pollution data and
health data. Feature engineering is used to create and optimize air quality and
health features. In order to merge these datasets residential address,
community/county/block/city and hospital/school address are used. Using
residence address or any location becomes a spatial problem when the Air
Quality Monitoring (AQM) stations are concentrated in urban areas within the
regions and an overlap in the AQM stations in urban areas coverage area, which
raises the question that how to associate the patients with the relevant AQM
station. Also, in most of the studies the distance of patients to the AQM
stations is also not taken into account. In this study, we propose a four-part
spatial feature engineering algorithm to find the coordinates for health data,
calculate distances with AQM stations and associate health records to the
nearest AQM station. Hence, removing the limitations of current air pollution
health datasets. The proposed algorithm is applied as a case study in Klang
Valley, Malaysia. The results show that the proposed algorithm can generate air
pollution health dataset efficiently and the algorithm also provides the radius
facility to exclude the patients who are situated far away from the stations.

Anum Saeed

Papers

Air pollution and its health impacts in Malaysia: a review

Geographic Information System and Big Spatial Data: A Review and Challenges

Role of ICT for Community in Education During COVID-19

A Spatial Feature Engineering Algorithm for Creating Air Pollution Health Datasets