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Environmental health risk management is a systematic engineering task, engaging multiple disciplines from the academic and government sectors. Reducing environmental health risks has become one of the key targets in the United Nations Sustainable Development Goals (SDGs). This target has been translated into public policies at many jurisdictional levels (Yue et al., 2020). To design region-specific and targeted policy initiatives, understanding how environmental health risks are spatially distributed and temporally resolved is fundamental.

Along with the advances in high-resolution pollution mapping and projection, environmental risk assessment and management have been performed at a highly granular scale (Caplin et al., 2019). For instance, taking advantage of the fine air pollution dataset, recent research efforts have evaluated the health burdens of historical air pollution exposure or cost-benefits of pollution control policies at finer-scale administrative units and grids (Liu et al., 2017Ou et al., 2020). These assessments reveal expensive health costs of air pollution exposure and highlight that the health outcomes of air pollution are unevenly distributed across regions and more evident among vulnerable populations (Colmer et al., 2020).

Even though detailed geospatial mappings of air pollution capture local patterns, they do not necessarily represent individual-level unique exposure experiences and health outcomes. Personal exposure to air pollution can be influenced by a range of behavioral factors such as mobility patterns and self-protective actions (Tainio et al., 2021). Individual cofactors such as risk perceptions of air pollutants, baseline health conditions, and socio–economic status also influence the pollution-related health impacts (Piel et al., 2020). Dramatic variations in health risks, therefore, occur even within small spatial units such as blocks and neighborhoods. Thus, individual-level exposure and health assessment are critical in advancing the engineering management of air pollution at granular spatiotemporal scales and informing targeted local policies for reducing pollution-related health risks.

To perform air pollution health risk assessment and management at the individual level, first, gathering datasets relevant to personal exposure experiences and health outcomes is important. For example, portable sensor technology is widely applied, in which participants carry the sensors to measure their real-time locations and micro-environmental exposure (Su et al., 2017). These monitors can illustrate individual time–activity patterns and assess personal-specific pollution exposure during outdoor activities such as daily commuting and exercises (Dons et al., 2017). These activities finally form direct flows of datasets on human behaviors and are made available to researchers for more nuanced characterizations of environmental risk.

In this comment, we summarize different types of individual-level data and outline pathways through which the data may advance air pollution health risks assessment. We then review representative studies revolving around these aspects and showcase how abundant information at the individual level improves environmental health risk management. We finally detail the challenges and uncertainties in this rapidly growing field and highlight the priorities in future research. The aim is to motivate local policy actions and foster collective research efforts to promote public health.


Megaprojects are a critical aspect of socio–economic development that can have huge effects on local communities, the environment, society, politics, or locals’ way of life (Zeng et al., 2015Denicol et al., 2020). Megaproject social responsibility (MSR) refers to “the policies and practices of stakeholders through the whole project lifecycle that reflect responsibilities for the well-being of the wide society” (Zeng et al., 2015). MSR governance refers to socially responsible actions of relevant stakeholders to alleviate and eliminate a megaproject’s negative effects on socio–economic and environmental outcomes during the megaproject’s entire lifecycle (Lin et al., 2017Ma et al., 2017), such as poverty reduction, human rights protection, social philanthropy, and environmental protection (Zeng et al., 2015). For large international contractors, differences between the decision-making scenarios of international megaprojects in host countries and those in their home countries are huge (Javernick-Will and Scott, 2010Cramton et al., 2021). Differences in political, cultural, economic, and regulatory contexts can lead to differences in the content of MSR, as well as in that of corporate social responsibility (Maignan and Ralston, 2002Matten and Moon, 2008). Consequently, MSR governance is challenging for international contractors. Good performance in MSR might contribute to the sustainability of megaprojects, whereas the absence of MSR governance in international megaprojects might generate huge losses for international contractors (Ma et al., 2017Petkova and van der Putten, 2020Leviker, 2021). Therefore, we argue that MSR governance can improve the quality of international megaprojects and reduce conflict among different parties in host countries (Campbell et al., 2012Zhou and Mi, 2017Ma et al., 2021).


In the majority of the previous works on discrete-event stochastic systems, they have been assumed to have independent input processes. However, in many applications, these input processes can be highly correlated. Furthermore, the performance measures of the systems with correlated inputs can be significantly different from those with independent inputs. In this paper, we provide an overview on some commonly used methods for modeling correlated input processes, and we discuss the difficulties and possible future research topics in the study of discrete-event stochastic systems with correlated inputs.

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