In order to describe the mode of operation of a networked system, a systems dynamics algorithm was designed for the topic of air pollution control. With the help of a matrix multiplication, this systems dynamics approach facilitates assessment of necessary measures for improved air quality (and its effects) to be simulated and assessed in a practical-minded quick overview. It turns out that for a desired long-term improvement in air quality measures that can be derived from the field of social sciences are at least as effective as technological measures that address the emitter directly. This is due to the fact that control loops become effective in a networked system which, in the case of amplification, can unleash a great effect while necessitating only relatively little effort.
Top1. Introduction
The linkage between data science and systems analysis is a very clear one: on the one hand, fine-tuned data facilitate modelling and thus yield more exact results, on the other hand, detected patterns in time-space can provide a direction in which key interactions between parameters can be detected (Antoniou et al., 2018, Bhardwaj et al., 2015; Gvishiani et al., 2016; Kazieva et al., 2020; Lei et al., 2015; Mondal, 2016; Salazar et al., 2017; Song et al., 2020; Steinwandter & Herwig, 2019; Tripakis, 2018; Weinand et al., 2021).
Moreover, the general architecture of any modelling task in systems analysis and systems dynamics requires understanding, which of the potentially contributing factors can actually exert substantial impact on the results of a model, and hence co-determine meaning of that (quantitatively described) world view (Bentur et al., 2021; Finelli & Narasimhan, 2020; Lee et al., 2020; Medford et al., 2018; Nannapaneni et al., 2015; Tedeschi, 2019; Yang et al., 2015).
A third argument for the relevance of systems dynamics is the need for suitable parametrisation of detected interactions: this needs sound basis drawn from data analysis (Bennett & Clark, 1994; Dominiczak & Khansa, 2018; Idreos & Kraska, 2019; Liebovitch et al., 2019; Parnell et al., 2021; Sheikh et al., 2021; Šoštarić et al., 2021; Wickramage, 2017; Zanin, et al., 2017).
Within systems science, the topic of urban air quality is among the most traditional themes because it allows clear systems borders when modelling, namely the city’s borders. Within a city, the most dynamic contributing factors can easily be identified, and thus a quick list of remedies can be established – which will suitably lead to rapid decision making on the municipal level. Such a mathematical tool should be short, concise, transparent, easy to understand, quickly manoeuvrable and thus quickly gain sympathy of municipal policy makers such as a mayor and key clerks, who traditionally have a leaning to practical-minded operationalism.
Data scientists are invited to see that (i) a quick conceptual look (ii) based on modelling experience allows to demonstrate the usefulness of gathering data on the local levels.
As topical example after the aggressive war attack by Russia on Ukraine, even states of sustainable peace were modelled (Liebovitch et al., 2019), thus shedding light on the importance of peace and empathy for global conviviality.
After this general justification of our theme’s presence in this encyclopedia, we turn to the concrete theme: In the field of environmental protection and in any transdisciplinary education, it often becomes apparent that the effect of a certain action is not limited to the directly intended effect (Sterman, 2000; Coyle, 2000; Senge, 1990; Forrester, 1971; Aschemann, 2004; Ahamer and Kumpfmüller, 2013). Side effects can, via detours, spark a much greater dynamic in the overall system than direct effects. Such a characteristic occurs with closed control loops, which can build themselves up (positive feedback) or also stabilize (negative feedback).