Francesco Pilla

End-user Perspective of Low-cost Sensors for Outdoor Air Pollution Monitoring

Low-cost sensor technology can potentially revolutionise the area of air pollution monitoring by providing high-density spatiotemporal pollution data. Such data can be utilised for supplementing traditional pollution monitoring, improving exposure estimates, and raising community awareness about air pollution. However, data quality remains a major concern that hinders the widespread adoption of low-cost sensor technology. Unreliable data may mislead unsuspecting users and potentially lead to alarming consequences such as reporting acceptable air pollutant levels when they are above the limits deemed safe for human health. This article provides scientific guidance to the end-users for effectively deploying low-cost sensors for monitoring air pollution and peoples exposure, while ensuring reasonable data quality. We review the performance characteristics of several low-cost particle and gas monitoring sensors and provide recommendations to end-users for making proper sensor selection by summarizing the capabilities and limitations of such sensors. The challenges, best practices, and future outlook for effectively deploying low-cost sensors, and maintaining data quality are also discussed. For data quality assurance, a two-stage sensor calibration process is recommended, which includes laboratory calibration under controlled conditions by the manufacturer supplemented with routine calibration checks performed by the end-user under final deployment conditions. For large sensor networks where routine calibration checks are impractical, statistical techniques for data quality assurance should be utilised. Further advancements and adoption of sophisticated mathematical and statistical techniques for sensor calibration, fault detection, and data quality assurance can indeed help to realise the promised benefits of a low-cost air pollution sensor network.

Multi-GCM ensembles performance for climate projection on a GIS platform

Climate impact studies especially in the field of hydrology often depend on climate change projections at fine spatial resolution. General circulation models (GCMs), which are the tools for estimating future climate scenarios, run on a very coarse scale, so the output from GCMs need to be downscaled to obtain a finer spatial resolution. This paper aims to present GIS platform as a downscaling environment through a suggested algorithm, which applies statistical downscaling models to multidimensional GCM-Ensembles simulations. Climate change projections for the Shannon River catchment in Ireland were developed for several climate variables from multi-GCM ensembles for three future time intervals forcing by different Representative Concentration Pathways (RCP): all these processes are implemented in a GIS platform through designed and developed GIS-based algorithm. This algorithm is used as a downscaling tool in GIS environment, which is unprecedented in literature. Statistical downscaling methods were used in the projection process after a particular verification and performance evaluation using several techniques such as Taylor diagram for each GCM-ensembles within independent sub-periods. The established statistical relationships were used to predict the response of the future climate from simulated climate model changes of the coarse scale variables. Significant changes in temperature, precipitation, wind speed, solar radiation and relative humidity were projected at a very fine spatial scale. It was concluded that the main source of uncertainty was related to the GCMs simulation and selection. In addition, it was obvious to conclude that GIS platform is an efficient tool for spatial downscaling using raster data forms.

Land use scenarios and projections simulation using an integrated GIS cellular automata algorithms

Over the years, urban growth models have proven to be effective in describing and estimating urban development and have consequently proven to be valuable for informed urban planning decision. Therefore, this paper investigates the implementation of an urban growth Cellular automata (CA) model using a GIS platform as a support tool for city planners, economists, urban ecologists and resource managers to help them establish decision making strategies and planning towards urban sustainable development. The area used as a test case is the River Shannon Basin in Ireland. This paper investigates the spatio-temporally varying effects of urbanization using a combined method of CA and GIS rasterization. The results generated from Cellular automata model indicated that the historical urban growth patterns in the River Shannon Basin area, in considerable part, be affected by distance to district centres, distance to roads, slope, neighbourhood effect, population density, and environmental factors with relatively high levels of explanation of the spatial variability. The optimal factors and the relative importance of the driving factors varied over time, thus, providing a valuable insight into the urban growth process. The developed model for Shannon catchment has been calibrated, validated, and used for predicting the future land use scenarios for the future time intervals 2020, 2050 and 2080. By involving natural and socioeconomic variables, the developed Cellular automata (CA) model had proved to be able to reproduce the historical urban growth process and assess the consequence of future urban growth. This paper presented as a novel application to the integrated CA-GIS model using a complicated land use dynamic system for Shannon catchment. The major conclusion from this paper was that land use simulation and projection without GIS rasterization formats cannot perform a multi-class, multi factors analysis which makes high accuracy simulation is impossible.

Spatially distributed potential evapotranspiration modeling and climate projections

Evapotranspiration integrates energy and mass transfer between the Earths surface and atmosphere and is the most active mechanism linking the atmosphere, hydrosphsophere, lithosphere and biosphere. This study focuses on the fine resolution modeling and projection of spatially distributed potential evapotranspiration on the large catchment scale as response to climate change. Six potential evapotranspiration designed algorithms, systematically selected based on a structured criteria and data availability, have been applied and then validated to long-term mean monthly data for the Shannon River catchment with a 50m2 cell size. The best validated algorithm was therefore applied to evaluate the possible effect of future climate change on potential evapotranspiration rates. Spatially distributed potential evapotranspiration projections have been modeled based on climate change projections from multi-GCM ensembles for three future time intervals (2020, 2050 and 2080) using a range of different Representative Concentration Pathways producing four scenarios for each time interval. Finally, seasonal results have been compared to baseline results to evaluate the impact of climate change on the potential evapotranspiration and therefor on the catchment dynamical water balance. The results present evidence that the modeled climate change scenarios would have a significant impact on the future potential evapotranspiration rates. All the simulated scenarios predicted an increase in potential evapotranspiration for each modeled future time interval, which would significantly affect the dynamical catchment water balance. This study addresses the gap in the literature of using GIS-based algorithms to model fine-scale spatially distributed potential evapotranspiration on the large catchment systems based on climatological observations and simulations in different climatological zones. Providing fine-scale potential evapotranspiration data is very crucial to assess the dynamical catchment water balance to setup management scenarios for the water abstractions. This study illustrates a transferable systematic method to design GIS-based algorithms to simulate spatially distributed potential evapotranspiration on the large catchment systems.

Performance-based Seismic Risk Assessment of Urban Systems

ABSTRACT Disaster risk mitigation has become a urgent global need. Similar to other natural hazards, earthquakes may cause significant damage on a large scale. In Europe and in other regions with dense urbanization, seismic events can heavily impact historical city centers due to the several structural fragilities. These centers are often part of the worldwide cultural heritage and their preservation is considered a strategic issue. Furthermore, earthquakes may have severe negative short-term economic effects on the impacted communities and adverse longer-term consequences for economic growth. For this reason, the development of an efficient approach for urban seismic risk assessment becomes essential. An original approach is proposed, based on performance concepts and multidisciplinary perspectives. The procedure is applied for validation to the city center of Concordia Sulla Secchia (Italy), damaged by the 2012 Pianura Padana Earthquake (PPE), comparing predicted damage scenarios with the actual post-seismic survey data.

Modelling of intra-urban variability of prevailing ambient noise at different temporal resolution

Abstract The impact of temporal aspects of noise data on model development and intra-urban variability on environmental noise levels are often ignored in the development of models used to predict its spatiotemporal variation within a city. Using a Land Use Regression approach, this study develops a framework which uses routine noise monitors to model the prevailing ambient noise, and to develop a noise variability map showing the variation within a city caused by land-use setting. The impact of data resolution on model development and the impact of meteorological variables on the noise level which are often ignored were also assessed. Six models were developed based on monthly, daily and hourly resolutions of both the noise and predictor data. Cross validation highlighted that only the hourly resolution model having 59%explanatory power of the observed data (adjusted R2) and a potential of explaining at least 0.47% variation of any independent dataset (cross validation R2), was a suitable candidate among all the developed models for explaining intraurban variability of noise. In the hourly model, regions with roads of high traffic volumes, with higher concentrations of heavy goods vehicles, and being close to activity centreswere found to have more impact on the prevailing ambient noise. Road lengthswere found to be the most influential predictors and identified as having an impact on the ambient noise monitors.

Using GIS based algorithms for GCMs' performance evaluation

General circulation models (GCMs) are used for estimating future climate scenarios, run on a very coarse scale, so the outputs from GCMs need to be downscaled to obtain a finer spatial resolution. This paper provides a methodology for GCM-Ensembles performance evaluation using a GIS platform by applying statistical spatial downscaling methods. Statistical downscaling methods were used in the projection process after validation and performance evaluation using several techniques such as Taylor diagram for each GCM-ensembles within independent sub-periods. Climate change projections for the Shannon River catchment in Ireland were developed for temperature and precipitation from multi-GCM ensembles for three future time intervals forcing by different Representative Concentration Pathways (RCP). The changes in temperature and precipitation were spatially projected at a very fine spatial scale.

Prediction of Indoor Air Exposure from Outdoor Air Quality Using an Artificial Neural Network Model for Inner City Commercial Buildings.

NO2 and particulate matter are the air pollutants of most concern in Ireland, with possible links to the higher respiratory and cardiovascular mortality and morbidity rates found in the country compared to the rest of Europe. Currently, air quality limits in Europe only cover outdoor environments yet the quality of indoor air is an essential determinant of a person’s well-being, especially since the average person spends more than 90% of their time indoors. The modelling conducted in this research aims to provide a framework for epidemiological studies by the use of publically available data from fixed outdoor monitoring stations to predict indoor air quality more accurately. Predictions are made using two modelling techniques, the Personal-exposure Activity Location Model (PALM), to predict outdoor air quality at a particular building, and Artificial Neural Networks, to model the indoor/outdoor relationship of the building. This joint approach has been used to predict indoor air concentrations for three inner city commercial buildings in Dublin, where parallel indoor and outdoor diurnal monitoring had been carried out on site. This modelling methodology has been shown to provide reasonable predictions of average NO2 indoor air quality compared to the monitored data, but did not perform well in the prediction of indoor PM2.5 concentrations. Hence, this approach could be used to determine NO2 exposures more rigorously of those who work and/or live in the city centre, which can then be linked to potential health impacts.

National scale assessment of total trihalomethanes in Irish drinking water

Ireland reported the highest non-compliance with respect to total trihalomethanes (TTHMs) in drinking water across the 27 European Union Member States for the year 2010. We carried out a GIS-based investigation of the links between geographical parameters and catchment land-uses with TTHMs concentrations in Irish drinking water. A high risk catchment map was created using peat presence, rainfall (>1400 mm) and slope (<5%) and overlain with a map comprising the national dataset of routinely monitored TTHM concentrations. It appeared evident from the map that the presence of peat, rainfall and slope could be used to identify catchments at high risk to TTHM exceedances. Furthermore, statistical analyses highlighted that the presence of peat soil with agricultural land was a significant driver of TTHM exceedances for all treatment types. PARAFAC analysis from three case studies identified a fluorophore indicative of reprocessed humic natural organic matter as the dominant component following treatment at the three sites. Case studies also indicated that (1) chloroform contributed to the majority of the TTHMs in the drinking water supplies and (2) the supply networks contributed to about 30 μg L-1 of TTHMs.

Attitudes to systemic risk: The impact of flood risk on the housing market in Dublin

The concatenated effects of increased frequency of intense precipitations due to climate change and anthropogenic impacts in the form of construction in floodplains, channel straightening and increased presence of impermeable surfaces are increasing the incidence of floods in urban areas. This paper investigates behavioral responses to a natural hazard (flooding) by examining residential property values. The results of this investigation can be used to develop benefit/cost studies to assess the economic merits of policies that mitigates the risk of floods by using the residential housing market as a proxy for estimating these values since the choice of where to live often includes the choice of hazard level. The methodology described here also provides a mechanism for testing consumer behavior under uncertainty. This study uses a hedonic property price function to estimate the effects of flood hazards on residential property values. The study utilizes data from 158,890 residential home sales in Dublin, Ireland between 2006 and 2015. This area experienced significant flooding in October 2011. GIS is used to spatially characterize the houses included in the analysis by linking them to the following set of parameters included into the baseline regression: house price, house type and size (number of bedrooms and bathrooms), when it was on the market, and its location. Once the baseline regression model is built, then the variables included in it are regressed against the flood-risk. The distance between a set of amenities and the properties is also calculated using GIS. Results show that a house located within a floodplain has a lower market value than an equivalent house located outside the floodplain. Finally, the benefits resulting from the use of GIS-based spatial indicators of properties in hedonic regression models to quantify the accessibility to amenities as network travel distances are also demonstrated.