Amy W. Ando and Noelwah R. Netusil
Green stormwater infrastructure (GSI), a decentralized approach for managing stormwater that uses natural systems or engineered systems mimicking the natural environment, is being adopted by cities around the world to manage stormwater runoff. The primary benefits of such systems include reduced flooding and improved water quality. GSI projects, such as green roofs, urban tree planting, rain gardens and bioswales, rain barrels, and green streets may also generate cobenefits such as aesthetic improvement, reduced net CO2 emissions, reduced air pollution, and habitat improvement. GSI adoption has been fueled by the promise of environmental benefits along with evidence that GSI is a cost-effective stormwater management strategy, and methods have been developed by economists to quantify those benefits to support GSI planning and policy efforts. A body of multidisciplinary research has quantified significant net benefits from GSI, with particularly robust evidence regarding green roofs, urban trees, and green streets. While many GSI projects generate positive benefits through ecosystem service provision, those benefits can vary with details of the location and the type and scale of GSI installation. Previous work reveals several pitfalls in estimating the benefits of GSI that scientists should avoid, such as double counting values, counting transfer payments as benefits, and using values for benefits like avoided carbon emissions that are biased. Important gaps remain in current knowledge regarding the benefits of GSI, including benefit estimates for some types of GSI elements and outcomes, understanding how GSI benefits last over time, and the distribution of GSI benefits among different groups in urban areas.
Hans Keune and Timo Assmuth
Framing and dealing with complexity are crucially important in environment and human health science, policy, and practice. Complexity is a key feature of most environment and human health issues, which by definition include aspects of the environment and human health, both of which constitute complex phenomena. The number and range of factors that may play a role in an environment and human health issue are enormous, and the issues have a multitude of characteristics and consequences. Framing this complexity is crucial because it will involve key decisions about what to take into account when addressing environment and human health issues and how to deal with them. This is not merely a technical process of scientific framing, but also a methodological decision-making process with both scientific and societal implications. In general, the benefits and risks related to such issues cannot be generalized or objectified, and will be distributed unevenly, resulting in health and environmental inequalities. Even more generally, framing is crucial because it reflects cultural factors and historical contingencies, perceptions and mindsets, political processes, and associated values and worldviews. Framing is at the core of how we as humans relate to, and deal with, environment and human health, as scientists, policymakers, and practitioners, with models, policies, or actions.
Knowledge of the important role that the environment plays in determining human health predates the modern public health era. However, the tendency to see health, disease, and their determinants as attributes of individuals rather than characteristics of communities meant that the role of the environment in human health was seldom accorded sufficient importance during much of the 20th century. Instead, research began to focus on specific risk factors that correlated with diseases of greatest concern, i.e., the non-communicable diseases such as cardiovascular disease, asthma, and diabetes. Many of these risk factors (e.g., smoking, alcohol consumption, and diet) were aspects of individual lifestyle and behaviors, freely chosen by the individual. Within this individual-centric framework of human health, the standard economic model for human health became primarily the Grossman model of health and health care demand.
In this model, an individual’s health stock may be increased by investing in health (by consuming health services, for example) or decreased by endogenous (age) or exogenous (smoking) individual factors. Within this model, individuals used their available resources, their budget, to purchase goods and services that either increased or decreased their health stock. Grossman’s model provides a consumption-based approach to human health, where individuals purchase goods and services required to improve their individual health in the marketplace. Grossman’s model of health assumes that the goods and services required to optimize good health can be purchased through market-based interactions and that these goods and services are optimally priced—that the value of the goods and services are reflected in their price.
In reality, many types of goods and services that are good for human health are not available to purchase, or if they are available they are undervalued in the free market. Across the environmental and health literature, these goods and services are, today, broadly referred to as “ecosystem services for human health.” However, the quasi-public good nature of ecosystem services for human health means that the private market will generate a suboptimal environment for both individual and public health outcomes. In the face of continued austerity and scarce public resources, understanding the role of the environment in human health may help to alleviate future health care demand by decreasing (or increasing) environmental risk (or benefits) associated with health outcomes. However, to take advantage of the role that the environment plays in human health requires a fundamental reorientation of public health policy and spending to include environmental considerations.
Paolo Vineis and Federica Russo
While genomics has been founded on accurate tools that lead to a limited amount of classification error, exposure assessment in epidemiology is often affected by large error. The “environment” is in fact a complex construct that encompasses chemical exposures (e.g., to carcinogens); biological agents (viruses, or the “microbiome”); and social relationships. The “exposome” concept was then put forward to stress the relatively poor development of appropriate tools for exposure assessment when applied to the study of disease etiology. Three layers of the exposome have been proposed: “general external” (including social capital, stress and psychology); “specific external” (including chemicals, viruses, radiation, etc.); and “internal” (including for example metabolism and gut microflora). In addition, there are at least three properties of the exposome: (a) it is based on a refinement of tools to measure exposures (including internal measurements in the body); (b) it involves a broad definition of “exposure” or environment, including overarching concepts at a societal level; and (c) it involves a temporal component (i.e., exposure is analyzed in a life-course perspective). The conceptual and practical challenge is how the different layers (i.e., general, specific external, and internal) connect to each other in a causally meaningful sequence. The relevance of this question pertains to the translation of science into policy—for example, if experiences in early life impact on the adult risk of disease, and on the quality of aging, how is distant action to be incorporated in biological causal models and into policy interventions? A useful causal theory to address scientific and policy question about exposure is based on the concept of information transmission. Such a theory can explain how to connect the different layers of the exposome in a life-course temporal frame and helps identify the best level for intervention (molecular, individual, or population level). In this context epigenetics plays a key role, partly because it explains the long-distance persistence of epigenetic changes via the concept of “epigenetic memory.”
Caroline A. Ochieng, Cathryn Tonne, Sotiris Vardoulakis, and Jan Semenza
Household air pollution from use of solid fuels (biomass fuels and coal) is a major problem in low and middle income countries, where 90% of the population relies on these fuels as the primary source of domestic energy. Use of solid fuels has multiple impacts, on individuals and households, and on the local and global environment. For individuals, the impact on health can be considerable, as household air pollution from solid fuel use has been associated with acute lower respiratory infections, chronic obstructive pulmonary disease, lung cancer, and other illnesses. Household-level impacts include the work, time, and high opportunity costs involved in biomass fuel collection and processing. Harvesting and burning biomass fuels affects local environments by contributing to deforestation and outdoor air pollution. At a global level, inefficient burning of solid fuels contributes to climate change.
Improved biomass cookstoves have for a long time been considered the most feasible immediate intervention in resource-poor settings. Their ability to reduce exposure to household air pollution to levels that meet health standards is however questionable. In addition, adoption of improved cookstoves has been low, and there is limited evidence on how the barriers to adoption and use can be overcome. However, the issue of household air pollution in low and middle income countries has gained considerable attention in recent years, with a range of international initiatives in place to address it. These initiatives could enable a transition from biomass to cleaner fuels, but such a transition also requires an enabling policy environment, especially at the national level, and new modes of financing technology delivery. More research is also needed to guide policy and interventions, especially on exposure-response relationships with various health outcomes and on how to overcome poverty and other barriers to wide-scale transition from biomass fuels to cleaner forms of energy.
Giovanni Lo Iacono and Gordon L. Nichols
The introduction of pasteurization, antibiotics, and vaccinations, as well as improved sanitation, hygiene, and education, were critical in reducing the burden of infectious diseases and associated mortality during the 19th and 20th centuries and were driven by an improved understanding of disease transmission. This advance has led to longer average lifespans and the expectation that, at least in the developed world, infectious diseases were a problem of the past. Unfortunately this is not the case; infectious diseases still have a significant impact on morbidity and mortality worldwide. Moreover, the world is witnessing the emergence of new pathogens, the reemergence of old ones, and the spread of antibiotic resistance. Furthermore, effective control of infectious diseases is challenged by many factors, including natural disasters, extreme weather, poverty, international trade and travel, mass and seasonal migration, rural–urban encroachment, human demographics and behavior, deforestation and replacement with farming, and climate change.
The importance of environmental factors as drivers of disease has been hypothesized since ancient times; and until the late 19th century, miasma theory (i.e., the belief that diseases were caused by evil exhalations from unhealthy environments originating from decaying organic matter) was a dominant scientific paradigm. This thinking changed with the microbiology era, when scientists correctly identified microscopic living organisms as the pathogenic agents and developed evidence for transmission routes. Still, many complex patterns of diseases cannot be explained by the microbiological argument alone, and it is becoming increasingly clear that an understanding of the ecology of the pathogen, host, and potential vectors is required.
There is increasing evidence that the environment, including climate, can affect pathogen abundance, survival, and virulence, as well as host susceptibility to infection. Measuring and predicting the impact of the environment on infectious diseases, however, can be extremely challenging. Mathematical modeling is a powerful tool to elucidate the mechanisms linking environmental factors and infectious diseases, and to disentangle their individual effects. A common mathematical approach used in epidemiology consists in partitioning the population of interest into relevant epidemiological compartments, typically individuals unexposed to the disease (susceptible), infected individuals, and individuals who have cleared the infection and become immune (recovered). The typical task is to model the transitions from one compartment to another and to estimate how these populations change in time. There are different ways to incorporate the impact of the environment into this class of models. Two interesting examples are water-borne diseases and vector-borne diseases. For water-borne diseases, the environment can be represented by an additional compartment describing the dynamics of the pathogen population in the environment—for example, by modeling the concentration of bacteria in a water reservoir (with potential dependence on temperature, pH, etc.). For vector-borne diseases, the impact of the environment can be incorporated by using explicit relationships between temperature and key vector parameters (such as mortality, developmental rates, biting rate, as well as the time required for the development of the pathogen in the vector).
Despite the tremendous advancements, understanding and mapping the impact of the environment on infectious diseases is still a work in progress. Some fundamental aspects, for instance, the impact of biodiversity on disease prevalence, are still a matter of (occasionally fierce) debate. There are other important challenges ahead for the research exploring the potential connections between infectious diseases and the environment. Examples of these challenges are studying the evolution of pathogens in response to climate and other environmental changes; disentangling multiple transmission pathways and the associated temporal lags; developing quantitative frameworks to study the potential effect on infectious diseases due to anthropogenic climate change; and investigating the effect of seasonality. Ultimately, there is an increasing need to develop models for a truly “One Health” approach, that is, an integrated, holistic approach to understand intersections between disease dynamics, environmental drivers, economic systems, and veterinary, ecological, and public health responses.
Erin N. Haynes, Lisa McKenzie, Stephanie A. Malin, and John W. Cherrie
Technological advances in directional well drilling and hydraulic fracturing have enabled extraction of oil and gas from once unobtainable geological formations. These unconventional oil and gas extraction (UOGE) techniques have positioned the United States as the fastest-growing oil and gas producer in the world. The onset of UOGE as a viable subsurface energy abstraction technology has also led to the rise of public concern about its potential health impacts on workers and communities, both in the United States and other countries where the technology is being developed. Herein we review in the national and global impact of UOGE from a historical perspective of occupational and public health. Also discussed are the sociological interactions between scientific knowledge, social media, and citizen action groups, which have brought wider attention to the potential public health implications of UOGE.
Global environmental change amplifies and creates pressures that shape human migration. In the 21st century, there has been increasing focus on the complexities of migration and environmental change, including forecasts of the potential scale and pace of so-called environmental migration, identification of geographic sites of vulnerability, policy implications, and the intersections of environmental change with other drivers of human migration. Migration is increasingly viewed as an adaptive response to climatic and environmental change, particularly in terms of livelihood vulnerability and risk diversification. Yet the adaptive potential of migration will be defined in part by health outcomes for migrating populations. There has been limited examination, however, of the health consequences of migration related to environmental change.
Migration related to environmental change includes diverse types of mobility, including internal migration to urban areas, cross-border migration, forced displacement following environmental disaster, and planned relocation—migration into sites of environmental vulnerability; much-debated links between environmental change, conflict, and migration; immobile or “trapped” populations; and displacement due to climate change mitigation and decarbonization action. Although health benefits of migration may accrue, such as increased access to health services or migration away from sites of physical risk, migration—particularly irregular (undocumented) migration and forced displacement—can amplify vulnerabilities and present risks to health and well-being. For diverse migratory pathways, there is the need to anticipate, respond to, and ameliorate population health burdens among migrants.
Maria Cristina Fossi and Cristina Panti
A vigorous effort to identify and study sentinel species of marine ecosystem in the world’s oceans has developed over the past 50 years. The One Health concept recognizes that the health of humans is connected to the health of animals and the environment. Species ranging from invertebrate to large marine vertebrates have acted as “sentinels” of the exposure to environmental stressors and health impacts on the environment that may also affect human health. Sentinel species can signal warnings, at different levels, about the potential impacts on a specific ecosystem. These warnings can help manage the abiotic and anthropogenic stressors (e.g., climate change, chemical and microbial pollutants, marine litter) affecting ecosystems, biota, and human health.
The effects of exposure to multiple stressors, including pollutants, in the marine environment may be seen at multiple trophic levels of the ecosystem. Attention has focused on the large marine vertebrates, for several reasons. In the past, the use of large marine vertebrates in monitoring and assessing the marine ecosystem has been criticized. The fact that these species are pelagic and highly mobile has led to the suggestion that they are not useful indicators or sentinel species. In recent years, however, an alternative view has emerged: when we have a sufficient understanding of differences in species distribution and behavior in space and time, these species can be extremely valuable sentinels of environmental quality.
Knowledge of the status of large vertebrate populations is crucial for understanding the health of the ecosystem and instigating mitigation measures for the conservation of large vertebrates. For example, it is well known that the various cetacean species exhibit different home ranges and occupy different habitats. This knowledge can be used in “hot spot” areas, such as the Mediterranean Basin, where different species can serve as sentinels of marine environmental quality. Organisms that have relatively long life spans (such as cetaceans) allow for the study of chronic diseases, including reproductive alterations, abnormalities in growth and development, and cancer. As apex predators, marine mammals feed at or near the top of the food chain. As the result of biomagnification, the levels of anthropogenic contaminants found in the tissues of top predators and long-living species are typically high. Finally, the application of consistent examination procedures and biochemical, immunological, and microbiological techniques, combined with pathological examination and behavioral analysis, has led to the development of health assessment methods at the individual and population levels in wild marine mammals. With these tools in hand, investigators have begun to explore and understand the relationships between exposures to environmental stressors and a range of disease end points in sentinel species (ranging from invertebrates to marine mammals) as an indicator of ecosystem health and a harbinger of human health and well-being.
Lora Fleming, Niccolò Tempini, Harriet Gordon-Brown, Gordon L. Nichols, Christophe Sarran, Paolo Vineis, Giovanni Leonardi, Brian Golding, Andy Haines, Anthony Kessel, Virginia Murray, Michael Depledge, and Sabina Leonelli
Big data refers to large, complex, potentially linkable data from diverse sources, ranging from the genome and social media, to individual health information and the contributions of citizen science monitoring, to large-scale long-term oceanographic and climate modeling and its processing in innovative and integrated “data mashups.” Over the past few decades, thanks to the rapid expansion of computer technology, there has been a growing appreciation for the potential of big data in environment and human health research.
The promise of big data mashups in environment and human health includes the ability to truly explore and understand the “wicked environment and health problems” of the 21st century, from tracking the global spread of the Zika and Ebola virus epidemics to modeling future climate change impacts and adaptation at the city or national level. Other opportunities include the possibility of identifying environment and health hot spots (i.e., locations where people and/or places are at particular risk), where innovative interventions can be designed and evaluated to prevent or adapt to climate and other environmental change over the long term with potential (co-) benefits for health; and of locating and filling gaps in existing knowledge of relevant linkages between environmental change and human health. There is the potential for the increasing control of personal data (both access to and generation of these data), benefits to health and the environment (e.g., from smart homes and cities), and opportunities to contribute via citizen science research and share information locally and globally.
At the same time, there are challenges inherent with big data and data mashups, particularly in the environment and human health arena. Environment and health represent very diverse scientific areas with different research cultures, ethos, languages, and expertise. Equally diverse are the types of data involved (including time and spatial scales, and different types of modeled data), often with no standardization of the data to allow easy linkage beyond time and space variables, as data types are mostly shaped by the needs of the communities where they originated and have been used. Furthermore, these “secondary data” (i.e., data re-used in research) are often not even originated for this purpose, a particularly relevant distinction in the context of routine health data re-use. And the ways in which the research communities in health and environmental sciences approach data analysis and synthesis, as well as statistical and mathematical modeling, are widely different.
There is a lack of trained personnel who can span these interdisciplinary divides or who have the necessary expertise in the techniques that make adequate bridging possible, such as software development, big data management and storage, and data analyses. Moreover, health data have unique challenges due to the need to maintain confidentiality and data privacy for the individuals or groups being studied, to evaluate the implications of shared information for the communities affected by research and big data, and to resolve the long-standing issues of intellectual property and data ownership occurring throughout the environment and health fields. As with other areas of big data, the new “digital data divide” is growing, where some researchers and research groups, or corporations and governments, have the access to data and computing resources while others do not, even as citizen participation in research initiatives is increasing. Finally with the exception of some business-related activities, funding, especially with the aim of encouraging the sustainability and accessibility of big data resources (from personnel to hardware), is currently inadequate; there is widespread disagreement over what business models can support long-term maintenance of data infrastructures, and those that exist now are often unable to deal with the complexity and resource-intensive nature of maintaining and updating these tools.
Nevertheless, researchers, policy makers, funders, governments, the media, and members of the general public are increasingly recognizing the innovation and creativity potential of big data in environment and health and many other areas. This can be seen in how the relatively new and powerful movement of Open Data is being crystalized into science policy and funding guidelines. Some of the challenges and opportunities, as well as some salient examples, of the potential of big data and big data mashup applications to environment and human health research are discussed.