The occurrence of harmful algal blooms (HABs) in coastal and inland waters has a significant impact on societies. This complex biogeophysical phenomenon becomes further complicated due to the impact of climate change. This review summarizes the research performed in recent years in the direction of climate change on three lake parameters, viz. lake temperature, precipitation, runoff, and lake ice, which impacts the lake ecology and, in turn, impacts the HABs. The present paper also reviews the research work related to the relationship between climate change and HABs. The purpose of this study is to provide the researchers with the opportunity to understand the current research in the direction of climate change and HABs so they can contribute effectively to one of the most important phenomena that will severely impact water quality in the future warmer climate, in coastal as well as inland water bodies. Furthermore, this work aims to discuss how HABs will change in the future warmer climate.

Harmful Algal Blooms lead to multi-billion-dollar losses in the United States due to shellfish closures, fish mortalities, and reluctance to consume seafood. Therefore, an improved early seasonal prediction of harmful algal blooms severity is important. Conventional methods for harmful algal blooms prediction using nutrient loading as the primary driver have been found to be less accurate during extreme bloom years. Here we show that a machine learning approach using observed nutrient loading, and large-scale climate indices can improve the harmful algal blooms prediction in Lake Erie. Moreover, the seasonal prediction of harmful algal blooms can be completed by early June, before the expected peak in harmful algal bloom activity from July to October. This improved early seasonal prediction can provide timely information to policymakers for adopting proper planning and mitigation strategies such as restrictions in harvesting and help in monitoring toxins in shellfish to keep contaminated products off the market.

The adverse health impacts of climate change have been well documented. It is increasingly apparent that the impacts are disproportionately higher in urban populations, especially underserved communities. Studies have linked urbanization and air pollution with health impacts, but the exacerbating role of urban heat islands (UHI) in the context of neurodegenerative diseases has not been well addressed. The complex interplay between climate change, local urban air pollution, urbanization, and a rising population in cities has led to the byproduct of increased heat stress in urban areas. Some urban neighborhoods with poor infrastructure can have excessive heat even after sunset, increasing internal body temperature and leading to hyperthermic conditions. Such conditions can put individuals at higher risk of stroke by creating a persistent neuroinflammatory state, including, in some instances, Alzheimer’s Disease (AD) phenotypes. Components of the AD phenotype, such as amyloid beta plaques, can disrupt long-term potentiation (LTP) and long-term depression (LTD), which can negatively alter the mesolimbic function and thus contribute to the pathogenesis of mood disorders. Furthermore, although a link has not previously been established between heat and Parkinson’s Disease (PD), it can be postulated that neuroinflammation and cell death can contribute to mitochondrial dysfunction and thus lead to Lewy Body formation, which is a hallmark of PD. Such postulations are currently being presented in the emerging field of ‘neurourbanism’. This study highlights that: (i) the impact of urban climate, air pollution and urbanization on the pathogenesis of neurodegenerative diseases and mood disorders is an area that needs further investigation; (ii) urban climate- health studies need to consider the heterogeneity in the urban environment and the impact it has on the UHI. In that, a clear need exists to go beyond the use of airport-based representative climate data to a consideration of more spatially explicit, high-resolution environmental datasets for such health studies, especially as they pertain to the development of locally-relevant climate adaptive health solutions. Recent advances in the development of super-resolution (downscaled climate) datasets using computational tools such as convolution neural networks (CNNs) and other machine learning approaches, as well as the emergence of urban field labs that generate spatially explicit temperature and other environmental datasets across different city neighborhoods, will continue to become important. Future climate – health studies need to develop strategies to benefit from such urban climate datasets that can aid the creation of localized, effective public health assessments and solutions.