TRAPSim: an agent-based model to estimate personal exposure to non-exhaust road emissions in central Seoul

Non-exhaust emissions (NEEs) from brake and tyre wear cause detrimental health effects, yet their relationship with mobility has not been examined rigorously. We constructed an agent-based traffic simulator to illustrate the coupled problems of emissions, behaviour, and the estimated exposure to PM10 for groups of drivers and subway commuters in Seoul CBD. Having calibrated the parameters, the results regarding the air quality revealed that roughly 25–30% of the roadside PM10 was significantly higher than the background PM10. Additionally, compared to intra-urban cars, pedestrians who commuted for longer periods of time and were exposed to more ambient particles suffered significant health losses; however, drivers only became aware of the health risk when PM10 levels were consistently high for a few days. Compared to the business-as-usual scenario of vehicle entry, a 90% vehicle restriction was able to reduce PM10 by 18–24% and cut the percentage of resident drivers who were at risk. However, it was not effective for subway commuters. Using an agent-based traffic simulator in a health context can provide insights into how exposure and health effects can vary depending on the time of exposure and the form of transportation

Defining mangrove-fisheries: A typology from the Perancak Estuary, Bali, Indonesia.

This study develops a definition of what mangrove-fisheries can encompass, incorporating a broad range of their possible characteristics. A detailed case study was conducted to develop a typology of mangrove-fishing in the Perancak Estuary, Bali, Indonesia, using interview surveys to investigate the fishing activities associated with mangroves. This case study demonstrated the complexity that a mangrove-fishery can entail, where fishing is connected to the mangrove forest by fishers of multiple sectors, functions, locations and temporal scales. Through a comparison with other mangrove-fishing communities in Bali, it also highlighted that mangrove-fisheries are variable even when in close proximity. With particular reference to this case study, a framework was developed as a flexible tool for identifying the multiple dimensions of a mangrove-fishery in a local context. Following this framework should encourage researchers and managers to look outside of the groups of fishers traditionally expected to benefit from mangrove fishing. This will enable the development of a broader definition of mangrove-fisheries in a site specific way. Identifying the full scope of fishers that contribute to or benefit from a mangrove-fishery is the first step towards building management measures that reflect the interests of groups of fishers that may otherwise remain under-represented. This is in line with international efforts for sustainability, especially in promoting small-scale fishers' access to sustainable resources under the UN Sustainable Development Goals

Leafless roughness of complex tree morphology using terrestrial LiDAR

Strategies for extracting roughness parameters from riparian forests need to address the issue that the trees are more than just stems and that in large rivers flow can rise into the canopy. Remote sensing information with 3-D capabilities such as lidar can be used to extract information on trees. However, first and last pulse airborne lidar data are insufficient to characterize the complex vertical structure of vegetation because by definition, there are few data at intermediate levels. Terrestrial laser scanning (TLS) is used in this study to define complex structures at a millimetric scanning resolution for the purpose of extracting canopy parameters relevant for the parameterization of the flow resistance equations. We will mainly be concerned with the projected area of leafless trees, estimating the total tree dimensions using several different methods. These include manipulating mass point cloud data obtained from TLS to create stage-dependent projected areas through complex meshing techniques and voxelization. Stage-dependent projected areas were defined for natural and planted poplar forests in the riparian zone of the Garonne and Allier rivers in southern and central France, respectively. Roughness values for planted poplar forests dominant in many western European river floodplains range from Manning's n = 0.037–0.094 and n = 0.140–0.330 for below-canopy flow (2 m) and extreme in-canopy flow (8 m), respectively. Roughness values for natural poplar forests ranged from n = 0.066–0.210 and n = 0.202–0.720 for below-canopy flow (2 m) and extreme in-canopy flow (8 m), respectively

An improvement in MATSim computing time for large-scale travel behaviour microsimulation

Abstract: Coupling activity-based models with dynamic traffic assignment appears to form a promising approach to investigating travel demand. However, such an integrated framework is generally time-consuming, especially for large-scale scenarios. This paper attempts to improve the performance of these kinds of integrated frameworks through some simple adjustments using MATSim as an example. We focus on two specific areas of the model—replanning and time stepping. In the first case we adjust the scoring system for agents to use in assessing their travel plans to include only agents with low plan scores, rather than selecting agents at random, as is the case in the current model. Secondly, we vary the model time step to account for network loading in the execution module of MATSim. The city of Baoding, China is used as a case study. The performance of the proposed methods was assessed through comparison between the improved and original MATSim, calibrated using Cadyts. The results suggest that the first solution can significantly decrease the computing time at the cost of slight increase of model error, but the second solution makes the improved MATSim outperform the original one, both in terms of computing time and model accuracy; Integrating all new proposed methods takes still less computing time and obtains relatively accurate outcomes, compared with those only incorporating one new method

MadingleyR: An R package for mechanistic ecosystem modelling

Abstract: Aim: Mechanistic general ecosystem models are used to explore fundamental ecological dynamics and to assess possible consequences of anthropogenic and natural disturbances on ecosystems. The Madingley model is a mechanistic general ecosystem model (GEM) that simulates a coherent global ecosystem, consisting of photo‐autotrophic and heterotrophic life, based on fundamental ecological processes. The C++ implementation of the Madingley model delivers fast computational performance, but it (a) limits the userbase to researchers that are familiar with the intricacies of C++ programming, (b) has limited possibility to change model settings and provide model outputs required to address specific research questions, and (c) has limited reproducibility of simulation experiments. The aim of this paper is to present an R package of the Madingley model to aid with increasing the accessibility and flexibility of the model. Innovation: The MadingleyR R package streamlines the installation procedure and supports all major operating systems. MadingleyR enables users to combine multiple consecutive simulations, making case study specific modifications to MadingleyR objects along the way. Default input files are available from the package and study‐specific inputs can be easily loaded from the R environment. MadingleyR also provides functions to plot and summarize MadingleyR outputs. We provide a comprehensive description of the MadingleyR functions and workflow. We also demonstrate the applicability of the MadingleyR package using three case studies: (a) simulating the cascading effects of the loss of mega‐herbivores on food‐web structure, (b) simulating the impacts of increased land‐use intensity on the total biomass of different feeding guilds by restricting the total vegetation biomass available for feeding and (c) simulating the impacts of an intensive land‐use scenario on a continental scale. Main conclusions: The MadingleyR package provides direct accessibility to simulations with the mechanistic ecosystem model Madingley and is flexible in its application without a loss in performance

Using agent-based modelling to simulate social-ecological systems across scales

Agent-based modelling (ABM) simulates Social-Ecological-Systems (SESs) based on the decision-making and actions of individual actors or actor groups, their interactions with each other, and with ecosystems. Many ABM studies have focused at the scale of villages, rural landscapes, towns or cities. When considering a geographical, spatially-explicit domain, current ABM architecture is generally not easily translatable to a regional or global context, nor does it acknowledge SESs interactions across scales sufficiently; the model extent is usually determined by pragmatic considerations, which may well cut across dynamical boundaries. With a few exceptions, the internal structure of governments is not included when representing them as agents. This is partly due to the lack of theory about how to represent such as actors, and because they are not static over the time-scales typical for social changes to have significant effects. Moreover, the relevant scale of analysis is often not known a priori, being dynamically determined, and may itself vary with time and circumstances. There is a need for ABM to cross the gap between micro-scale actors and larger-scale environmental, infrastructural and political systems in a way that allows realistic spatial and temporal phenomena to emerge; this is vital for models to be useful for policy analysis in an era when global crises can be triggered by small numbers of micro-level actors. We aim with this thought-piece to suggest conceptual avenues for implementing ABM to simulate SESs across scales, and for using big data from social surveys, remote sensing or other sources for this purpose

Different Modelling Purposes

How one builds, checks, validates and interprets a model depends on its ‘purpose’. This is true even if the same model code is used for different purposes. This means that a model built for one purpose but then used for another needs to be re-justified for the new purpose and this will probably mean it also has to be re-checked, re-validated and maybe even re-built in a different way. Here we review some of the different purposes for a simulation model of complex social phenomena, focusing on seven in particular: prediction, explanation, description, theoretical exploration, illustration, analogy, and social interaction. The paper looks at some of the implications in terms of the ways in which the intended purpose might fail. This analysis motivates some of the ways in which these ‘dangers’ might be avoided or mitigated. It also looks at the ways that a confusion of modelling purposes can fatally weaken modelling projects, whilst giving a false sense of their quality. These distinctions clarify some previous debates as to the best modelling strategy (e.g. KISS and KIDS). The paper ends with a plea for modellers to be clear concerning which purpose they are justifying their model against

A Spatial Cluster Analysis of Tractor Overturns in Kentucky from 1960 to 2002

Agricultural tractor overturns without rollover protective structures are the leading cause of farm fatalities in the United States. To our knowledge, no studies have incorporated the spatial scan statistic in identifying high-risk areas for tractor overturns. The aim of this study was to determine whether tractor overturns cluster in certain parts of Kentucky and identify factors associated with tractor overturns.A spatial statistical analysis using Kulldorff's spatial scan statistic was performed to identify county clusters at greatest risk for tractor overturns. A regression analysis was then performed to identify factors associated with tractor overturns.The spatial analysis revealed a cluster of higher than expected tractor overturns in four counties in northern Kentucky (RR = 2.55) and 10 counties in eastern Kentucky (RR = 1.97). Higher rates of tractor overturns were associated with steeper average percent slope of pasture land by county (p = 0.0002) and a greater percent of total tractors with less than 40 horsepower by county (p<0.0001).This study reveals that geographic hotspots of tractor overturns exist in Kentucky and identifies factors associated with overturns. This study provides policymakers a guide to targeted county-level interventions (e.g., roll-over protective structures promotion interventions) with the intention of reducing tractor overturns in the highest risk counties in Kentucky

Exposure to non-exhaust emission in Central Seoul using an agent-based framework

Non-exhaust emission (NEE) from brake and tyre wear cause deleterious effects on human health, but relationship with mobility has not been thoroughly examined. We construct an in silico agent-based traffic simulator for Central Seoul to illustrate the coupled problems of emissions, behaviour, and the estimated exposure to PM10 (particles less than 10 microns in size) for groups of drivers and subway commuters. The results show that significant extra particulates relative to the background exist along roadways where NEEs contributed some 40% of the roadside PM10. In terms of health risk, 88% of resident drivers had an acute health effect in late March but that kind of emergence rarely happened. By contrast, subway commuters’ health risk peaked at a maximum of 30% with frequent oscillations whenever the air pollution episodes occurred. A 90% vehicle restriction scenario reduced PM10 by 18-24%, and reduced the resident driver’s risk by a factor of 2, but not effective for subway commuters as the group generally walked through background areas rather than along major roadways. Using an agent-based traffic simulator in a health context can give insights into how exposure and health outcomes can depend on the time of exposure and the mode of transport