Computational Biology|Data Collection and Processing

Computational biology and data science have undergone tremendous expansion in recent years, resulting from increased computational power and accessibility of...

Data Science Methods in Biology (BIOL0053)

BASIC SPATIAL ANALYSIS: VECTOR DATA| COORDINATE SYSTEMS AND DATA PRE-PROCESSING

The purpose of this module is to introduce you to energy modelling analysis where there is spatial and temporal elements which can reveal important and...

Spatial Analysis of Energy Data (BENV0093)

The purpose of this module is to introduce you to energy modelling analysis where there is spatial and temporal elements which can reveal important and relevant relations between energy potential, resources and demand.

Geospatial energy analysis does not only brings understanding of the energy potential of a particular region but it also gives a solid base for feasibility studies, policy making and socio-environmental impacts. This cutting-edge discipline uses available georeferenced data from sensors, records, models and meters to recognise regional and global patterns that can be used to combat climate change and energy poverty.

This module will provide you with core GIS (Geographic Information System) skills using QGIS and R (the module assumes that you are familiarised and skilled using R). It will give you both technical and analytical skills. To achieve this, the module will combine theoretical (lecture) and practical (tutorials) knowledge with surgeries. From week six, invited guests will share their experience and knowledge with geospatial analysis in the different energy sectors.

Module summary:

This module addresses indoor air quality (IAQ) issues in buildings, the implications for health &amp; wellbeing, and methods of assessment,...

Indoor Air Quality in Buildings (BENV0040)

Module summary:

This module addresses indoor air quality (IAQ) issues in buildings, the implications for health & wellbeing, and methods of assessment, remediation/avoidance and how to design for healthy IAQ. A range of airborne pollutants from both indoor and outdoor sources will be explored, their impacts on physical and mental health, as well as aspects such as wellbeing and human performance. Students will gain a critical understanding of pollutant source identification, methods of pollutant modelling and monitoring, and remediation/avoidance strategies in building design/operation. Course work will be based on data analysis from monitoring and simulation modelling of the case study building.

Module learning outcomes:

At the end of this course, students will:

• Discuss factors affecting poor indoor air quality in buildings, including sources, ventilation and filtration strategies, and occupant behaviour. 

• List the most significant pollutants affecting indoor air quality (both of indoor and outdoor origin), outlining their health impacts, sources and prevalence when known

• Describe how to investigate and evaluate indoor air quality (IAQ) in buildings, using monitoring techniques and data analysis.

• Discuss the tensions that exist between energy performance and IAQ criteria and how to apply optimal strategies in building design and ventilation for good IAQ.

• Outline how computer modelling can be used to predict indoor air quality in buildings and to identify design strategies to improve IAQ