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This is a Masters Thesis completed at University College Dublin, Ireland in 2017 which involved augmenting an off-the-shelf electric bike with sensors to enable new services to be delivered to cyclists in cities. The application of primary interest was to control the cyclist's ventilation rate based on the concentration of local air pollutants. Detailed modelling and system design is presented for our Cyberphysical system which consisted of a modified BTwin e-bike, Cycle Analyst sensors, the cyclist themselves, a Bluetooth connected smartphone and our algorithms. Control algorithms to regulate the proportion of power the cyclist provided as a proxy for their ventilation rate were proposed and validated in a basic way, which were later proven significantly further in Further Work (see IEEE Transactions on Intelligent Transportation Systems paper: https://ieeexplore.ieee.org/abstract/document/8357977). The basic idea was to provide more electrical assistance to cyclists in areas of high air pollution to reduce the cyclist ventilation rate and thereby the amount of air pollutants inhaled. This presents an interesting control challenge due to the human-in-the-loop characteristics and the potential for impactful real life applications. A background literature review is provided on energy as it relates to cycling and some other applications are also discussed. A link to a video which demonstrates the system is provided, and also to a blog published by IBM Research about the system.

The Internet of Things (IoT) is a paradigm characterized by a network of embedded sensors and services. These sensors are incorporated to collect various information, track physical conditions, e.g., waste bins' status, and exchange data with different centralized platforms. The need for such sensors is increasing; however, proliferation of technologies comes with various challenges. For example, how can IoT and its associated data be used to enhance waste management? In smart cities, an efficient waste management system is crucial. Artificial Intelligence (AI) and IoT-enabled approaches can empower cities to manage the waste collection. This work proposes an intelligent approach to route recommendation in an IoT-enabled waste management system given spatial constraints. It performs a thorough analysis based on AI-based methods and compares their corresponding results. Our solution is based on a multiple-level decision-making process in which bins' status and coordinates are taken into account to address the routing problem. Such AI-based models can help engineers design a sustainable infrastructure system., Comment: 11

Social media services such as TripAdvisor and Foursquare can provide opportunities for users to exchange their opinions about urban green space (UGS). Visitors can exchange their experiences with parks, woods, and wetlands in social communities via social networks. In this work, we implement a unified topic modeling approach to reveal UGS characteristics. Leveraging Artificial Intelligence techniques for opinion mining using the mentioned platforms (e.g., TripAdvisor and Foursquare) reviews is a novel application to UGS quality assessments. We show how specific characteristics of different green spaces can be explored by using a tailor-optimized sentiment analysis model. Such an application can support local authorities and stakeholders in understanding--and justification for--future urban green space investments., Comment: 6 pages, 4 figures

Forest fires impact on soil, water and biota resources has been widely researched. Although forest fires profoundly impact the atmosphere and air quality across the ecosystems, much less research has been developed to examine its impact on the current pandemic. In-situ air pollution data were utilized to examine the effects of the 2020 forest fire on atmosphere and coronavirus (COVID 19) casualties. The spatiotemporal concentrations of particulate matter (PM2.5 and PM10) and Nitrogen Dioxide (NO2) were collected from August 1 to October 30 for 2020 (fire year) and 2019 (reference year). Both spatial (Multiscale Geographically Weighted Regression) and non spatial (negative binomial regression) regression analysis was performed to assess the adverse effects of fire emission on human health. The in situ data led measurements showed that the maximum increases in PM2.5, PM10, and NO2 concentrations were clustered in the West Coastal fire-prone states during the August 1 to October 30 period. The average concentration of particulate matter (PM2.5 and PM10) and NO2 were increased in all the fire states affected badly by forest fires. The average PM2.5 concentration over the period was recorded as 7.9, 6.3, 5.5, and 5.2 for California, Colorado, Oregon, and Washington in 2019, which was increased up to 24.9, 13.4, 25, and 17 in 2020. Both spatial and non-spatial regression models exhibited a statistically significant association between fire emission and COVID 19 incidents. A total of 30 models were developed for analyzing the spatial non-stationary and local association between the predictor and response factors. All these spatial models have demonstrated a statistically significant association between fire emissions and COVID counts. More thorough research is needed to better understand the complex association between forest fire and human health., Comment: 35 pages

The increasing level of marine plastic pollution poses severe threats to the marine ecosystem and biodiversity. The present study attempted to explore the full functionality of open Sentinel satellite data and ML models for detecting and classifying floating plastic debris in Mytilene (Greece), Limassol (Cyprus), Calabria (Italy), and Beirut (Lebanon). Two ML models, i.e. Support Vector Machine (SVM) and Random Forest (RF) were utilized to carry out the classification analysis. In-situ plastic location data was collected from the control experiment conducted in Mytilene, Greece and Limassol, Cyprus, and the same was considered for training the models. Both remote sensing bands and spectral indices were used for developing the ML models. A spectral signature profile for plastic was created for discriminating the floating plastic from other marine debris. A newly developed index, kernel Normalized Difference Vegetation Index (kNDVI), was incorporated into the modelling to examine its contribution to model performances. Both SVM and RF were performed well in five models and test case combinations. Among the two ML models, the highest performance was measured for the RF. The inclusion of kNDVI was found effective and increased the model performances, reflected by high balanced accuracy measured for model 2 (~80% to ~98 % for SVM and ~87% to ~97 % for RF). Using the best-performed model, an automated floating plastic detection system was developed and tested in Calabria and Beirut. For both sites, the trained model had detected the floating plastic with ~99% accuracy. Among the six predictors, the FDI was found the most important variable for detecting marine floating plastic. These findings collectively suggest that high-resolution remote sensing imagery and the automated ML models can be an effective alternative for the cost-effective detection of marine floating plastic., Comment: 30 pages

Continued population growth and urbanization is shifting research to consider the quality of urban green space over the quantity of these parks, woods, and wetlands. The quality of urban green space has been hitherto measured by expert assessments, including in-situ observations, surveys, and remote sensing analyses. Location data platforms, such as TripAdvisor, can provide people's opinion on many destinations and experiences, including UGS. This paper leverages Artificial Intelligence techniques for opinion mining and text classification using such platform's reviews as a novel approach to urban green space quality assessments. Natural Language Processing is used to analyze contextual information given supervised scores of words by implementing computational analysis. Such an application can support local authorities and stakeholders in their understanding of and justification for future investments in urban green space.

The spatialization of socioeconomic data can be used and integrated with other sources of information to reveal valuable insights. Such data can be utilized to infer different variations, such as the dynamics of city dwellers and their spatial and temporal variability. This work focuses on such applications to explore the underlying association between socioeconomic characteristics of different geographical regions in Dublin, Ireland, and the number of confirmed COVID cases in each area. Our aim is to implement a machine learning approach to identify demographic characteristics and spatial patterns. Spatial analysis was used to describe the pattern of interest in Electoral Divisions (ED), which are the legally defined administrative areas in the Republic of Ireland for which population statistics are published from the census data. We used the most informative variables of the census data to model the number of infected people in different regions at ED level. Seven clusters detected by implementing an unsupervised neural network method. The distribution of people who have contracted the virus was studied., Comment: 11

Since December 2019, the world has been witnessing the gigantic effect of an unprecedented global pandemic called Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) - COVID-19. So far, 38,619,674 confirmed cases and 1,093,522 confirmed deaths due to COVID-19 have been reported. In the United States (US), the cases and deaths are recorded as 7,833,851 and 215,199. Several timely researches have discussed the local and global effects of the confounding factors on COVID-19 casualties in the US. However, most of these studies considered little about the time varying associations between and among these factors, which are crucial for understanding the outbreak of the present pandemic. Therefore, this study adopts various relevant approaches, including local and global spatial regression models and machine learning to explore the causal effects of the confounding factors on COVID-19 counts in the contiguous US. Totally five spatial regression models, spatial lag model (SLM), ordinary least square (OLS), spatial error model (SEM), geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR), are performed at the county scale to take into account the scale effects on modelling. For COVID-19 cases, ethnicity, crime, and income factors are found to be the strongest covariates and explain the maximum model variances. For COVID-19 deaths, both (domestic and international) migration and income factors play a crucial role in explaining spatial differences of COVID-19 death counts across counties. The local coefficient of determination (R2) values derived from the GWR and MGWR models are found very high over the Wisconsin-Indiana-Michigan (the Great Lake) region, as well as several parts of Texas, California, Mississippi and Arkansas.

Forest fires have a profound impact on the atmospheric environment and air quality across the ecosystems. The recent west coast forest fire in the United States of America (USA) has broken all the past records and caused severe environmental and public health burdens. As of middle September, nearly 6 million acres forest area were burned, and more than 25 casualties were reported so far. In this study, both satellite and in-situ air pollution data were utilized to examine the effects of this unprecedented wildfire on the atmospheric environment. The spatiotemporal concentrations of total six air pollutants, i.e. carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), particulate matter (PM2.5 and PM10), and aerosol index (AI), were measured for the periods of 15 August to 15 September for 2020 (fire year) and 2019 (reference year). The in-situ data-led measurements show that the highest increases in CO (ppm), PM2.5, and PM10 concentrations ({\mu}g/m3) were clustered around the west coastal fire-prone states, during the 15 August - 15 September period. The average CO concentration (ppm) was increased most significantly in Oregon (1147.10), followed by Washington (812.76), and California (13.17). Meanwhile, the concentration ({\mu}g/m3) in particulate matter (both PM2.5 and PM10), was increased in all three states affected severely by wildfires. Changes (positive) in both PM2.5 and PM10 were measured highest in Washington (45.83 and 88.47 for PM2.5 and PM10), followed by Oregon (41.99 and 62.75 for PM2.5 and PM10), and California (31.27 and 35.04 for PM2.5 and PM10). The average level of exposure to CO, PM2.5, and PM10 was also measured for all the three fire-prone states. The results of the exposure assessment revealed a strong tradeoff association between wildland fire and local/regional air quality standard.

Noise pollution is considered to be the third most hazardous pollution after air and water pollution by the World Health Organization (WHO). Short as well as long-term exposure to noise pollution has several adverse effects on humans, ranging from psychiatric disorders such as anxiety and depression, hypertension, hormonal dysfunction, and blood pressure rise leading to cardiovascular disease. One of the major sources of noise pollution is road traffic. The WHO reports that around 40% of Europe's population are currently exposed to high noise levels. This study investigates noise pollution in Dublin, Ireland before and after the lockdown imposed as a result of the COVID-19 pandemic. The analysis was performed using 2020 hourly data from 12 noise monitoring stations. More than 80% of stations recorded high noise levels for more that 60% of the time before the lockdown in Dublin. However, a significant reduction in average and minimum noise levels was observed at all stations during the lockdown period and this can be attributed to reductions in both road and air traffic movements., Comment: 20 pages, 8 figures

The demographic factors have a substantial impact on the overall casualties caused by the COVID-19. In this study, the spatial association between the key demographic variables and COVID-19 cases and deaths were analyzed using the spatial regression models. Total 13 (for COVID-19 case factor) and 8 (for COVID-19 death factor) key variables were considered for the modelling. Total five spatial regression models such as Geographically weighted regression (GWR), Spatial Error Model (SEM), Spatial Lag Model (SLM), Spatial Error_Lag model (SEM_SLM), and Ordinary Least Square (OLS) were performed for the spatial modelling and mapping of model estimates. The local R2 values, which suggesting the influences of the selected demographic variables on overall casualties caused by COVID-19, was found highest in Italy and the UK. The moderate local R2 was observed for France, Belgium, Netherlands, Ireland, Denmark, Norway, Sweden, Poland, Slovakia, and Romania. The lowest local R2 value for COVID-19 cases was accounted for Latvia and Lithuania. Among the 13 variables, the highest local R2 was calculated for total population (R2 = 0.92), followed by death crude death rate (R2 = 0.9), long time illness (R2 = 0.84), population with age >80 (R2 = 0.59), employment (R2 = 0.46), life expectancy at 65 (R2 = 0.34), crude birth rate (R2 = 0.31), life expectancy (R2 = 0.31), Population with age 65-80 (R2 = 0.29), Population with age 15-24 (R2 = 0.27), Population with age 25-49 (R2 = 0.27), Population with age 0-14 (R2 = 0.23), and Population with age 50-65 (R2 = 0.23), respectively., Comment: 45

The effect of transport-related pollution on human health is fast becoming recognised as a major issue in cities worldwide. Cyclists, in particular, face great risks, as they typically are most exposed to tail-pipe emissions. Three avenues are being explored worldwide in the fight against urban pollution: (i) outright bans on polluting vehicles and embracing zero tailpipe emission vehicles; (ii) measuring air-quality as a means to better informing citizens of zones of higher pollution; and (iii) developing smart mobility devices that seek to minimize the effect of polluting devices on citizens as they transport goods and individuals in our cities. Following this latter direction, in this paper we present a new way to protect cyclists from the effect of urban pollution. Namely, by exploiting the actuation possibilities afforded by pedelecs or e-bikes (electric bikes), we design a cyber-physical system that mitigates the effect of urban pollution by indirectly controlling the breathing rate of cyclists in polluted areas. Results from a real device are presented to illustrate the efficacy of our system., Comment: Title change: Previous title of this paper was: Cyberphysics, Pedelecs, and Pollution Mitigation. A longer version of this paper was accepted for publication in IEEE Transactions on Intelligent Transportation Systems, August 4th, 2017