Massey Documents by Type

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Microfibres and health: State of the evidence and research gaps
(Elsevier B V, 2025-08-01) Taptiklis P; Boulic M; Phipps R; van Heerden H; Shaw C
Microfibres are ubiquitous in the environment and there has been an increasing focus on health harms from them in recent decades. The current WHO guidelines defining health risks from microfibres focus on just the subset of microfibres that are inorganic and respirable. Recent studies have revealed large volumes of textile microfibres are present throughout the environment and that non-plastic microfibres are as common or more common than plastic microfibres. However, these are rarely included in the analysis of harms. This narrative review of textile microfibres sets out the state of our understanding of exposure to and harms from textile microfibres. We found that the epidemiological research reviewed here does not support the continued focus solely on the respiratory route of exposure nor only on plastic microfibres as hazardous to health. In fact, gastrointestinal as well as upper airway effects may also be increased by exposure to textile microfibres. Importantly, microfibres behave differently in the environment, and within the body in comparison to non-fibre particles, and therefore warrant separate investigation from particles and microplastics. The conclusion of this cross-disciplinary review is an urgent call for greater investigation of textile microfibres, separately from the also important issue of microplastics, and therefore, the inclusion of non-plastic fibre types in research going forward.
Organic contaminants in Ganga basin: from the Green Revolution to the emerging concerns of modern India.
(Elsevier B.V., 2021-02-17) Ghirardelli A; Tarolli P; Kameswari Rajasekaran M; Mudbhatkal A; Macklin MG; Masin R
The Ganga basin includes some of the most densely populated areas in the world, in a region characterized by extremely high demographic and economic growth rates. Although anthropogenic pressure in this area is increasing, the pollution status of the Ganga is still poorly studied and understood. In the light of this, we have carried out a systematic literature review of the sources, levels and spatiotemporal distribution of organic pollutants in surface water and sediment of the Ganga basin, including for the first time emerging contaminants (ECs). We have identified 61 publications over the past thirty years, with data on a total of 271 organic compounds, including pesticides, industrial chemicals, and by-products, artificial sweeteners, pharmaceuticals, and personal care products (PPCPs). The most studied organic contaminants are pesticides, whereas knowledge of industrial compounds and PPCPs, among which some of the major ECs, is highly fragmentary. Most studies focus on the main channel of the Ganga, the Yamuna, the Gomti, and the deltaic region, while most of the Ganga's major tributaries, and the entire southern part of the catchment, have not been investigated. Hotspots of contamination coincide with major urban agglomerations, including Delhi, Kolkata, Kanpur, Varanasi, and Patna. Pesticides levels have decreased at most of the sites over recent decades, while potentially harmful concentrations of polychlorinated biphenyls (PCBs), organotin compounds (OTCs), and some PPCPs have been detected in the last ten years. Considering the limited geographical coverage of sampling and number of analyzed compounds, this review highlights the need for a more careful selection of locations, compounds and environmental matrices, prioritizing PPCPs and catchment-scale, source-to-sink studies.
Machine learning based calibration techniques for low-cost air quality sensors : thesis for Doctor of Philosophy, Electronic and Computer Engineering, Massey University
(Massey University, 2024-05-28) Ali, Mohammad Sharafat
Breathable air is the single most essential element for life on earth. Polluted air poses numerous risks to health and the environment, especially in urban areas with large populations and many active sources of air pollution. Therefore, researchers from a wide range of disciplines have been working on mitigating the impact of air pollution. Monitoring ambient air pollution is one of the means to ensure public health safety, raise public awareness and build a sustainable urban environment. However, conventional air quality monitoring stations are mostly confined to a few locations due to their costly equipment and large sizes. As a result, although these monitoring stations provide accurate air pollution data, they can only offer a low-fidelity picture of air quality in a large city, leading to a poor spatial resolution of urban pollution data. Low-cost sensor (LCS) technologies aim to address this challenge and intend to make it possible to monitor air quality at a high spatio-temporal resolution. The pollutant data captured by these LCSs are less accurate than their conventional counterparts and thus require calibration techniques to improve their accuracy and reliability. Researchers have proposed different calibration methods and techniques to improve the accuracy of the LCSs, including machine learning based calibration models. This thesis investigates and proposes several machine learning-based calibration techniques and rigorously benchmarks their performance using a robust training, validation and testing method. Based on the findings, One Dimensional Convolutional Neural Network (1DCNN) and Gradient Boosting Regression (GBR) based calibration techniques provide consistently accurate performance. Both of these machine learning techniques, which have not been widely used or evaluated for low-cost ambient gas sensor calibration, can improve the state of the art. This research also demonstrates that readily available and previously unemployed co-variate data, namely the number of days the sensor has been deployed and the time of day at which the reading is taken, can significantly improve the accuracy of Machine Learning based calibration algorithms.

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