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Nature, Urban and Human Laboratory

A Multidisciplinary Research Lab Connecting Nature, Urban, and Human

Lab description

The NUH Lab is dedicated to advancing cutting-edge research at the intersection of the natural environment, urban environment, and human. Our multidisciplinary approach spans diverse fields, including environmental engineering, architectural engineering, civil engineering, mechanical engineering, sustainability engineering, and public health. Through collaborative research, we address complex challenges and pioneer innovative solutions that integrate these critical domains.

Lab contact

Jenny.Zhou@monash.edu

Lab facilities

The NUH Lab is equipped with a comprehensive suite of advanced resources tailored for both field-based and controlled experimental research. Our facilities include high-precision monitoring and measurement assets, the experimental chamber with fully adjustable environmental controls for human-centred studies, non-intrusive biosensing devices for tracking health and well-being, and dedicated simulation platforms and analytical tools. Together, these facilities enable us to push the boundaries of research across multiple disciplines.

Applications

Human-building interaction (HBI) View

We categorise the lab's capabilities into five key areas, each accompanied by a research project and a scholarly output to demonstrate the lab's practical applications (note that the research project and scholarly output may originate from different studies). While these examples offer a glimpse of our lab, they are not exhaustive. Our lab continues to expand its capabilities each year. For the most up-to-date information on NBH Lab and its ongoing projects, please contact Jenny.Zhou@monash.edu.

1. Environmental Monitoring

Description: Our lab is equipped with a wide array of technologies, including IoT-based systems and nationally accredited instruments, for precise and reliable environmental data collection.

Example Project: Monash Smart Manufacturing Hub (mSMH) – A Digital Twin Living Lab. This project evaluates the performance of a digital twin using energy-efficient thermal comfort in the built environment as a case study. The multimodal sensing network integrates environmental and human sensing to enhance the digital twin model.

Example Scholarly Output: Performance Evaluation of Low-Cost Air Quality Sensors: A Review (DOI: 10.1016/j.scitotenv.2021.151769)

2. Energy and Environmental Modelling

Description: We provide advanced software tools and databases for energy and environmental simulations, ranging from individual buildings to entire cities.

Example Project: Modelling the Human Factors of Energy Use in Melbourne Houses. This project aims to develop a digital suite to fully represent the “human dimension” in building energy and carbon performance. The goal is to refine an accredited model for energy and environmental simulations in residential dwellings.

Example Scholarly Output: Performance Gap in a Multi-Storey Student Accommodation Complex Built to Passivhaus Standard (DOI: 10.1016/j.buildenv.2021.107704)

3. Human Health and Well-being

Description: Our lab combines specialised instruments for tracking bioaerosols and other problematic substances with advanced data analytics to provide comprehensive assessments of human exposure and well-being in various environments.

Example Project: Mass Timber Wellness in Workplaces. This project collects environmental data, surveys, and interviews to develop metrics that measure workplace wellness and productivity in mass timber buildings.

Example Scholarly Output: Influence of Moisturizer and Relative Humidity on Human Emissions of Fluorescent Biological Aerosol Particles (DOI: 10.1111/ina.12349)

4. Biosensing and Cognition

Description: We utilise medical-grade biosensing tools alongside established methods for evaluating human physiological and cognitive responses in diverse built environments..

Example Project: Age-friendly Built Environment: Physical and Perceptual Gaps in IEQ. This study investigates the link between experience design and environmental psychology, using a case study from an aged care facility in Victoria, Australia.

Example Scholarly Output: A Systematic Review of Human Experience in Indoor Thermal Environments through Electroencephalogram (EEG) (DOI: 10.1016/j.buildenv.2024.111533)

5. Multi-attribute Analysis

Description: Our lab offers methodologies on multi-attribute decision analysis to guide decision-making and optimization in complex research and industrial projects..

Example Project: A Multi-Attribute Decision Analysis for Decommissioning Waste Management. This research develops a comprehensive framework to support decision-making in decommissioning waste management, enabling systematic comparisons of waste management strategies.

Example Scholarly Output: Multi-Criteria Decision Analysis for Sustainable Oil and Gas Infrastructure Decommissioning: A Systematic Review (DOI: 10.3390/su16167205)

Agriculture and food systems View

Global georeferenced data products (For more details please contact Fiona.Tang@monash.edu).

1. CROPGRIDS

CROPGRIDS is a global georeferenced dataset providing harvested and crop (physical) area of 173 crops for the year 2020, at a resolution of 0.05° (about 5.6km at the equator). Crop type name, harvested area and crop area definitions used in CROPGRIDS are aligned to the relevant UN-FAO commodities and land use definitions.

Link to dataset: https://doi.org/10.6084/m9.figshare.22491997
Link to data description: https://doi.org/10.1038/s41597-024-03247-7

Harvested area maps in CROPGRIDS for soybean.

2. NPKGRIDS

NPKGRIDS is a global georeferenced dataset providing fertilizer application rates for three main plant nutrients, nitrogen (N), phosphorus (P) and potassium (K) across 173 crops for the year 2020, at a resolution of 0.05° (about 5.6km at the equator). Crop type name, harvested area and crop area definitions used in NPKGRIDS are aligned to the relevant UN-FAO commodities and land use definitions.

Link to dataset: https://doi.org/10.6084/m9.figshare.24616050
Link to data description: TBA

Nitrogen fertilizer application rate in NPKGRIDS for soybean

3. PEST-CHEMGRIDS (v1)

PEST-CHEMGRIDS is a global georeferenced dataset providing the crop-specific application rates of 95 pesticide active ingredients at 5 arc-min resolution (about 10 km at the equator) for the year 2015, and projections to 2025.

Link to dataset: https://doi.org/10.6084/m9.figshare.7764014
Link to data description: https://doi.org/10.1038/s41597-019-0169-4

Glyphosate application rate in PEST-CHEMGRIDS (v1) for corn

4. SOIL-WATERGRIDS

SOIL-WATERGRIDS is a global georeferenced dataset providing dynamic changes in soil moisture and depth of water table over 45 years from 1970 to 2014 globally resolved at 0.25 × 0.25 degree resolution (about 30 × 30 km at the equator) along a 56 m deep soil profile. SOIL-WATERGRIDS estimates were obtained using the BRTSim model, a process-based model accounting for soil physical and hydraulic properties, land cover and use characteristics, and hydrometeorological variables to account for precipitation, ecosystem-specific evapotranspiration, snowmelt, surface runoff, and irrigation.

Link to BRTSim model: https://sites.google.com/site/thebrtsimproject/home
Link to dataset: https://zenodo.org/records/4997453
Link to data description: https://doi.org/10.1038/s41597-021-01032-4

Water table types included in SOIL-WATERGRIDS.

5. Pesticide budget and river discharge to ocean

This dataset includes the global georeferenced land runoff, soil residue in the root zone, river concentration, and discharge to oceans for 92 pesticides applied in agriculture. These variables were estimated using BRTSim model, a spatial- and temporal-explicit process-based environmental model.

Link to BRTSim model: https://sites.google.com/site/thebrtsimproject/home
Link to dataset: https://doi.org/10.6084/m9.figshare.20452791
Link to data description: https://doi.org/10.1038/s41586-023-06296-x

Pesticide concentration in rivers and discharge to ocean

6. Pesticide mixture in soils

This dataset includes four global maps of aggregated pesticides: (1) the total pesticide content in the topsoil of global croplands expressed in the unit of mg/kg-soil; (2) the number of pesticides detected in the topsoil; (3) the pesticide leaching rate to below the root zone expressed in the unit of mg/m2/year; and (4) the number of pesticides that exceeded a leaching rate of 1 mg/m2/year. The data were estimated using BRTSim model, a process-based spatial- and temporal-explicit environmental model.

Link to BRTSim model: https://sites.google.com/site/thebrtsimproject/home
Link to dataset: https://doi.org/10.6084/m9.figshare.12966323
Link to data description: https://doi.org/10.1088/1748-9326/abe5d6

Pesticide residue in the topsoil

7. Pesticide pollution risk

This dataset includes three global maps: (1) the pesticide risk scores map showing the exposure of agricultural land to pesticide pollution, (2) the active ingredient counts map quantifying the number of active ingredients posing pollution risk to agricultural land and showing the exposure of environment to pesticide mixtures, and (3) the regions of concern map identifying areas susceptible to pesticide pollution.

Link to dataset: https://doi.org/10.6084/m9.figshare.10302218
Link to data description: https://doi.org/10.1038/s41561-021-00712-5

Regions of concern map identifying areas susceptible to pesticide pollution

Engineering