Spatiotemporal and temporal forecasting of ambient air pollution levels through data-intensive hybrid artificial neural network models

Outdoor air pollution (AP) is a serious public threat which has been linked to severe respiratory and cardiovascular illnesses, and premature deaths especially among those residing in highly urbanised cities. As such, there is a need to develop early-warning and risk management tools to alleviate its effects. The main objective of this research is to develop AP forecasting models based on Artificial Neural Networks (ANNs) according to an identified model-building protocol from existing related works. Plain, hybrid and ensemble ANN model architectures were developed to estimate the temporal and spatiotemporal variability of hourly NO2 levels in several locations in the Greater London area. Wavelet decomposition was integrated with Multilayer Perceptron (MLP) and Long Short-term Memory (LSTM) models to address the issue of high variability of AP data and improve the estimation of peak AP levels. Block-splitting and crossvalidation procedures have been adapted to validate the models based on Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Willmott’s index of agreement (IA). The results of the proposed models present better performance than those from the benchmark models. For instance, the proposed wavelet-based hybrid approach provided 39.15% and 28.58% reductions in RMSE and MAE indices, respectively, on the performance of the benchmark MLP model results for the temporal forecasting of NO2 levels. The same approach reduced the RMSE and MAE indices of the benchmark LSTM model results by 12.45% and 20.08%, respectively, for the spatiotemporal estimation of NO2 levels in one site at Central London. The proposed hybrid deep learning approach offers great potential to be operational in providing air pollution forecasts in areas without a reliable database. The model-building protocol adapted in this thesis can also be applied to studies using measurements from other sites.Outdoor air pollution (AP) is a serious public threat which has been linked to severe respiratory and cardiovascular illnesses, and premature deaths especially among those residing in highly urbanised cities. As such, there is a need to develop early-warning and risk management tools to alleviate its effects. The main objective of this research is to develop AP forecasting models based on Artificial Neural Networks (ANNs) according to an identified model-building protocol from existing related works. Plain, hybrid and ensemble ANN model architectures were developed to estimate the temporal and spatiotemporal variability of hourly NO2 levels in several locations in the Greater London area. Wavelet decomposition was integrated with Multilayer Perceptron (MLP) and Long Short-term Memory (LSTM) models to address the issue of high variability of AP data and improve the estimation of peak AP levels. Block-splitting and crossvalidation procedures have been adapted to validate the models based on Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Willmott’s index of agreement (IA). The results of the proposed models present better performance than those from the benchmark models. For instance, the proposed wavelet-based hybrid approach provided 39.15% and 28.58% reductions in RMSE and MAE indices, respectively, on the performance of the benchmark MLP model results for the temporal forecasting of NO2 levels. The same approach reduced the RMSE and MAE indices of the benchmark LSTM model results by 12.45% and 20.08%, respectively, for the spatiotemporal estimation of NO2 levels in one site at Central London. The proposed hybrid deep learning approach offers great potential to be operational in providing air pollution forecasts in areas without a reliable database. The model-building protocol adapted in this thesis can also be applied to studies using measurements from other sites

Indoor environmental quality (IEQ) analysis of a low energy wind catcher with horizontally-arranged heat transfer devices

Windcatchers are natural ventilation systems based on the design of traditional architecture, intended to provide ventilation by manipulating pressure differentials around buildings induced by wind movement and temperature difference. Though the movement of air caused by the wind catcher will lead to a cooling sensation for occupants, the high air temperature in hot climates will result in little cooling or thermal discomfort to occupants. In order to improve the cooling performance by wind catchers, heat transfer devices were incorporated into the design. This work will investigate the indoor environment quality performance of a roof-mounted cooling windcatcher integrated with horizontally-arranged heat transfer devices (HHTD) using Computational Fluid Dynamics (CFD) and field test analysis. The windcatcher model was incorporated to a 5mx5mx3m test room model. The study employed the CFD code FLUENT with the standard k- model to conduct the steady-state RANS simulation. For the indoor CO2 concentration analysis, a simplified exhalation model was used and the room was filled with 12 occupants. The CO2 concentration analysis showed that the system was capable of delivering fresh air inside the space and lowering the CO2 levels. Thermal comfort analysis using the Predicted Mean Vote (PMV) was conducted whereby the measurements ranged from slightly-cool (-0.96) to slightly warm range (0.36 to 0.60). Field test measurements were carried out in the Ras-Al-Khaimah (RAK), UAE during the month of September. Numerical model was validated using experimental data and good agreement was observed between both methods of analysis

Hybrid artificial neural network models for effective prediction and mitigation of urban roadside NO2 pollution

Traffic-related air pollution has been a serious concern amongst policy-makers and the public due to its physiological and environmental impacts. An early warning system based on accurate forecasting tools must therefore be implemented to circumvent the adverse effects of exposure to major air pollutants. A multilayer perceptron neural network was trained and developed using air pollution and meteorological data over a two-year period from a monitoring site in Marylebone Road, Central London to predict roadside concentration values of NO2 24 hours ahead. Several hybrid models were also developed by applying feature selection techniques such as stepwise regression, principal component analysis, and Classification and Regression Trees to the neural network model. Most roadside pollutant variables, e.g., oxides of nitrogen, were found to be significant in predicting NO2. The statistical results reveal overall prediction superiority of the hybrid models to the standalone neural network model

A critical examination of the phenomenon of post-truth

In the contemporary era, the truth is generally being doubted. Various philosophers say that it is the result of postmodernism to which a new term—the Post-Truth— was created to describe this phenomenon. The term ‘post-truth’ became well-known after the Oxford Dictionary chose it as the “Word of the Year” in 2016. It serves as a term that perfectly describes and represents the occurrences in the contemporary era. This is a new phenomenon created from the information explosion by the modern media that sets out to confuse people between what is true and what is a lie.Many references of this phenomenon point to the events in 2016 when Trump won the US presidency. This led various thinkers to write about the disappearance of the shared objective in philosophical and political discourses. These discourses helped this paper in establishing and identifying the parallel events that are happening in the Philippines. The paper examined Duterte politics as a concrete example of the phenomenon of post-truth. The term was born from a sense of regret by those who worry that truth is being eclipsed. For some aspects, at least, the term presumes a point of view: that facts and truth are endangered in today’s political arena (Mclntyre, 2020). Thus, this research analyzed the origin, occurrence, and impact of the post-truth phenomenon. This paper is a critical examination as it identifies a new philosophical problem that affects the authority of truth. The post-truth phenomenon undermines, subverts, and devalues the truth. This paper discussed its elements to understand the phenomenon.This thesis cites various philosophers, including Hannah Arendt and George Orwell who are pointed as the harbingers of the phenomenon. It also discusses Jacques Derrida’s deconstruction, Michel Foucault’s regime of truth, and other contemporary philosophers. The paper basically argues that post-truth exists within our time. But, how it occurs in our time, particularly in the Philippines, is yet to be known

PGA(1)-induced apoptosis involves specific activation of H-Ras and N-Ras in cellular endomembranes

The cyclopentenone prostaglandin A1 (PGA1) is an inducer of cell death in cancer cells. However, the mechanism that initiates this cytotoxic response remains elusive. Here we report that PGA1 triggers apoptosis by a process that entails the specific activation of H- and N-Ras isoforms, leading to caspase activation. Cells without H- and N-Ras did not undergo apoptosis upon PGA1 treatment; in these cells, the cellular demise was rescued by overexpression of either H-Ras or N-Ras. Consistently, the mutant H-Ras-C118S, defective for binding PGA1, did not produce cell death. Molecular analysis revealed a key role for the RAF-MEK-ERK signaling pathway in the apoptotic process through the induction of calpain activity and caspase-12 cleavage. We propose that PGA1 evokes a specific physiological cell death program, through H- and N-Ras, but not K-Ras, activation at endomembranes. Our results highlight a novel mechanism that may be of potential interest for tumor treatment.status: publishe

Airborne microplastic monitoring: Developing a simplified outdoor sampling approach using pollen monitoring equipment

A novel, yet simple, airborne microplastic (MP) sampling approach using global pollen monitoring equipment was applied to identify, characterise and quantify outdoor airborne MPs for the first time. Modification of Burkard spore trap tape adhesive provided particle capture and facilitated downstream spectroscopy analysis. 36 polymer types were identified from a total of 21 days sampling using Burkard spore traps at two locations (United Kingdom and South Africa). MPs were detected in 95 % of daily samples. Mean MP particle levels were 2.0 ± 0.9 MP m-3 (11 polymer types) in Hull (U.K.), during March, 2.9 ± 2.0 MP m-3 (16 types) in Hull in July, and 11.0 ± 5.7 MP m-3 (29 types) in Gqeberha, (S.A.) in August 2023. The most abundant polymer type was nylon (Gqeberha). The approach was compared with two passive sampling methods whereby 27 polymer types were identified and of these, 6 types were above the limit of quantification (LOQ), with poly(methacrolein:styrene) (PMA/PS) the most abundant. Irregularly shaped MPs < 100 µm in length were predominant from all sampling approaches. For the first time, airborne MPs were chemically characterised and quantified using volumetric pollen sampling equipment, representing a viable approach for future airborne MP monitoring