Comparison of land use regression and random forests models on estimating noise levels in five Canadian cities

Comparative analysis and application of soft sensor models in domestic wastewater treatment for advancing sustainability

This study focuses on the development and evaluation of soft sensor models for predicting NH3-N values in a wastewater treatment process. The study compares the performance of linear regression (LR), neural networks (NN) and random forest regression (RFR) models. The proposed methodology involves optimizing the sequencing batch reactor process using artificial intelligence and an automatic control system. Real-time NH3-N values are obtained by inputting data from electronic conductivity and temperature sensors into the prediction models. Once the predicted NH3-N value falls below the effluent standard, the cycle ends, improving energy efficiency and sustainability by cutting down the agitator and aerator. The research results demonstrate that the RNN-based NH3-N soft sensor built in this study exhibits the best performance, which is promising for wastewater treatment process optimization and evaluation. The results show that sensor model NNR[0.5Y]H exhibits exceptional performance, utilizing recurrent neural network with 5-step input delays. Sensor NNR[0.5Y]H exhibits an R2 of 0.921, an RMSE of 6.110, and an MAE of 4.558. Based on the findings, recurrent neural network (RNN) variants emerge as the most effective modeling technique due to their ability to capture temporal dependencies and handle variable-length sequences. This study provides satisfied performance results for the NNR[0.5Y]H soft sensor model in NH3-N monitoring and process optimization in wastewater treatment, highlighting the effectiveness of recurrent neural networks and their contribution to improving interpretability, accuracy, and adaptability of soft sensor models.

Evaluating sound attenuation of single trees using 3D information

Urban tree belts reduce noise pollution, but limited research has focused on the mitigation potential of single trees. Identifying individual tree characteristics that influence noise propagation can assist in selecting trees to improve urban soundscapes at multiple scales. This study introduces a methodology to evaluate and predict the sound attenuation of single trees using 3D tree morphology data and sound observations. We extracted structural characteristics for 26 trees on Nanjing University's Xianlin campus from handheld terrestrial LiDAR. Second, the sound attenuation of each sample tree was quantified systematically using a sound source and a receiver. The sound level meter was placed in front of and behind each sample tree to record the received sound levels. The sound source was positioned 1.5m above ground to emit white noise, ensuring the front receiver recording sound levels of 55, 60, and 68 dBA. We established a support vector regression (SVR) with a linear (LN) kernel to predict the sound attenuation of single trees based on their 3D characteristics. Single trees yielded an insertion loss of 2-3 dBA, effectively eliminating sound above 500 Hz and increasing with the frequency. It is also interesting to note that the insertion loss increases with increasing source sound levels. Regression analysis revealed that an increase in crown leaf area index (β = 0.332, p < 0.01) and average leaf inclination (β = 0.168, p < 0.01) reduced sound significantly, indicating the tree canopy's predominant role in impeding sound propagation. The SVR-LN model, established using standardized parameters with statistical significance, exhibited strong predictive sound attenuation performance using tree characteristics (R2 = 0.74, RMSE = 0.38, and MSE = 0.15). This study addresses a research gap in the acoustic effects of single trees and provides a framework for accurately evaluating and predicting sound attenuation based on 3D characteristics. The findings can assist urban planners and policymakers in strategically planting trees to foster healthier and quieter living spaces for residents.

Random effect generalized linear model-based predictive modelling of traffic noise

Noise pollution is one of the negative consequences of growth and development in cities. Traffic noise pollution due to traffic growth is the main aspect that worsens city quality of life. Therefore, research around the world is being conducted to manage and reduce traffic noise. A number of traffic noise prediction models have been proposed employing fixed effect modelling approach considering each observation as independent; however, observations may have spatial and temporal correlations and unobserved heterogeneity. Random effect models overcome these problems. This study attempts to develop a random effect generalized linear model (REGLM) along with a machine learning random forest (RF) model to validate the results, concerning the parameters related to road, traffic and environmental conditions. Models were developed based on the experimental quantities in Delhi in year 2022-2023. Both the models performed comparably well in terms of coefficient of determination. Random forest models with R2= 0.75, whereas random effect generalized linear model had an R2= 0.70. REGLM model has the ability to quantify the effects of explanatory variables over traffic noise pollution and will be more helpful in prioritizing of resources and chalking out control strategies.

Reductions in community noise levels in vancouver, Canada, during pandemic lockdown and association with land cover type

BACKGROUND

Urban transportation noise is a major public concern because of its adverse effects on health. The determinants of urban noise exposure have not been widely explored but the "natural experiment" presented by the COVID-19 lockdowns presented a unique opportunity. This study examined the relationship between environmental characteristics and urban noise pollution during the COVID-19 related lockdown in Metro Vancouver, Canada, from March 21st to May 18th, 2020.

METHODS

We used noise exposure data from the Vancouver International Airport (YVR) noise management program, comparing the noise levels during "Phase One" of the COVID-19 lockdown in 2020 to the corresponding time period in 2019 from 21 Noise Monitoring Terminals (NMTs) located throughout Metro Vancouver. We modelled the relationship between the change in noise level and the physical NMT environments, including land cover, and total length of roads at four different time periods (24Hr, daytime, evening and nighttime) and within three different buffer zones (100 m, 250 m, and 500 m).

RESULTS

Of 59,472 hourly measurements of community noise, the 24-h noise level was reduced by an average of 2.20 dBA between 2019 and 2020. Higher proportions of greenspace, barren areas, and soil-cover around NMTs resulted in stronger noise reductions and higher density of building, pavement, and water weakened the amount of noise reduction. Proximity of high-volume traffic roads (highways) were associated with weaker noise reduction.

CONCLUSION

The COVID-19 related lockdown was associated with reduced noise in Metro Vancouver, and the relative reduction depended on the types of the environment surrounding the NMT. Future research on the effects of urban environmental characteristics on geographic inequality in noise levels and health consequences of the COVID-19 related lockdown is merited.

The association of bisphenol A and paraben exposure with sensorineural hearing loss in children

Bisphenol A (BPA) and parabens (PBs) are chemicals that are extensively used in personal care products (PCPs). In early childhood development, hearing is critical to speech and language development, communication, and learning. In vitro and in vivo, BPA/PBs exhibited neurotoxicity through elevated levels of oxidative stress. BPA also has the potential to be an ototoxicant. Therefore, this study aimed to determine the association of exposure to BPA/PBs with sensorineural hearing loss in children. A cross-sectional study based on hearing tests was conducted. This study enrolled 320 children aged 6-12 years from elementary school. Urinary BPA and PB concentrations were analyzed by using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Logistic regression models were employed to determine the association of BPA/PB exposure with sensorineural hearing loss. Children with sensorineural hearing loss had higher BPA concentrations than normal-hearing children (0.22 ng/ml vs. 0.10 ng/ml, p = 0.05). After adjustment for covariates, the risk of hearing loss at middle frequencies reached 1.83-fold (95% CI: 1.12-2.99) when BPA concentrations increased by 1 log10. The risk of slight hearing loss reached 2.24-fold (95% CI: 1.05-4.78) when children had a tenfold increase in ethyl paraben (EP) concentration. This study clarifies the role of exposure to BPA/PBs in hearing loss in children. Future research needs to be expanded to include cohort designs and nationwide studies to identify causality.

Critical windows of exposure to air pollution and gestational diabetes: assessing effect modification by maternal pre-existing conditions and environmental factors

BACKGROUND

Ambient air pollution has been associated with gestational diabetes (GD), but critical windows of exposure and whether maternal pre-existing conditions and other environmental factors modify the associations remains inconclusive.

METHODS

We conducted a retrospective cohort study of all singleton live birth that occurred between April 1^st 2006 and March 31^st 2018 in Ontario, Canada. Ambient air pollution data (i.e., fine particulate matter with a diameter ≤ 2.5 μm (PM_2.5), nitrogen dioxide (NO₂) and ozone (O₃)) were assigned to the study population in spatial resolution of approximately 1 km × 1 km. The Normalized Difference Vegetation Index (NDVI) and the Green View Index (GVI) were also used to characterize residential exposure to green space as well as the Active Living Environments (ALE) index to represent the active living friendliness. Multivariable Cox proportional hazards regression models were used to evaluate the associations.

RESULTS

Among 1,310,807 pregnant individuals, 68,860 incident cases of GD were identified. We found the strongest associations between PM_2.5 and GD in gestational weeks 7 to 18 (HR = 1.07 per IQR (2.7 µg/m³); 95% CI: 1.02 - 1.11)). For O₃, we found two sensitive windows of exposure, with increased risk in the preconception period (HR = 1.03 per IQR increase (7.0 ppb) (95% CI: 1.01 - 1.06)) as well as gestational weeks 9 to 28 (HR 1.08 per IQR (95% CI: 1.04 -1.12)). We found that women with asthma were more at risk of GD when exposed to increasing levels of O₃ (p- value for effect modification = 0.04). Exposure to air pollutants explained 20.1%, 1.4% and 4.6% of the associations between GVI, NDVI and ALE, respectively.

CONCLUSION

An increase of PM_2.5 exposure in early pregnancy and of O₃ exposure during late first trimester and over the second trimester of pregnancy were associated with gestational diabetes whereas exposure to green space may confer a protective effect.

Environmental noise and health in low-middle-income-countries: A systematic review of epidemiological evidence

Evidence of the health impacts from environmental noise has largely been drawn from studies in high-income countries, which has then been used to inform development of noise guidelines. It is unclear whether findings in high-income countries can be readily translated into policy contexts in low-middle-income-countries (LMICs). We conducted this systematic review to summarise noise epidemiological studies in LMICs. We conducted a literature search of studies in Medline and Web of Science published during 2009-2021, supplemented with specialist journal hand searches. Screening, data extraction, assessment of risk of bias as well as overall quality and strength of evidence were conducted following established guidelines (e.g. Navigation Guide). 58 studies were identified, 53% of which were from India, China and Bulgaria. Most (92%) were cross-sectional studies. 53% of studies assessed noise exposure based on fixed-site measurements using sound level meters and 17% from propagation-based noise models. Mean noise exposure among all studies ranged from 48 to 120 dB (L_eq), with over half of the studies (52%) reporting the mean between 60 and 80 dB. The most studied health outcome was noise annoyance (43% of studies), followed by cardiovascular (17%) and mental health outcomes (17%). Studies generally reported a positive (i.e. adverse) relationship between noise exposure and annoyance. Some limited evidence based on only two studies showing that long-term noise exposure may be associated with higher prevalence of cardiovascular outcomes in adults. Findings on mental health outcomes were inconsistent across the studies. Overall, 4 studies (6%) had "probably low", 18 (31%) had "probably high" and 36 (62%) had "high" risk of bias. Quality of evidence was rated as 'low' for mental health outcomes and 'very low' for all other outcomes. Strength of evidence for each outcome was assessed as 'inadequate', highlighting high-quality epidemiological studies are urgently needed in LMICs to strengthen the evidence base.

The Canadian Environmental Quality Index (Can-EQI): Development and calculation of an index to assess spatial variation of environmental quality in Canada's 30 largest cities

BACKGROUND

Multiple characteristics of the urban environment have been shown to influence population health and health-related behaviours, though the distribution and combined effects of these characteristics on health is less understood. A composite measure of multiple environmental conditions would allow for comparisons among different urban areas; however, this measure is not available in Canada.

OBJECTIVES

To develop an index of environmental quality for Canada's largest urban areas and to assess the influence of population size on index values.

METHODS

We conducted a systematic search of potential datasets and consulted with experts to refine and select datasets for inclusion. We identified and selected nine datasets across five domains (outdoor air pollution, natural environments, built environments, radiation, and climate/weather). Datasets were chosen based on known impacts on human health across the life course, complete geographic coverage of the cities of interest, and temporal alignment with the 2016 Canadian census. Each dataset was then summarized into dissemination areas (DAs). The Canadian Environmental Quality Index (Can-EQI) was created by summing decile ranks of each variable based on hypothesized relationships to health outcomes.

RESULTS

We selected 30 cities with a population of more than 100,000 people which included 28,026 DAs and captured approximately 55% of the total Canadian population. Can-EQI scores ranged from 21.1 to 88.9 out of 100, and in Canada's largest cities were 10.2 (95% CI: -10.7, -9.7) points lower than the smallest cities. Mapping the Can-EQI revealed high geographic variability within and between cities.

DISCUSSION

Our work demonstrates a valuable methodology for exploring variations in environmental conditions in Canada's largest urban areas and provides a means for exploring the role of environmental factors in explaining urban health inequalities and disparities. Additionally, the Can-EQI may be of value to municipal planners and decision makers considering the allocation of investments to improve urban conditions.

The Effect of Land-Use Categories on Traffic Noise Annoyance

Land-use categories are often used to define the exposure limits of national environmental noise policies. Often different guideline values for noise are applied for purely residential areas versus residential areas with mixed-use. Mixed-use includes living plus limited activities through crafts, commerce, trade, agriculture, and forestry activities. This differentiation especially when rating noise from road, railway, and air traffic might be argued by different expectations and therefore noise annoyance in those two categories while scientific evidence is missing. It should be tested on empirically derived data. Surveys from two studies in the state of Tyrol in urban and rural areas were retrospectively matched with spatial data to analyze the potential different influences on noise effects. Using non-parametric tests, the correlation between land-use category on self-reported noise sensitivity and noise annoyance was investigated. Exposure-response for the two analyzed land-use categories showed no significant impact on noise sensitivity and exposure-response relationships for the three traffic noise sources. Including only noise annoyance, there is not sufficient evidence to define different noise policies for those two land-use categories.

Spatial modelling and inequalities of environmental noise in Accra, Ghana

Noise pollution is a growing environmental health concern in rapidly urbanizing sub-Saharan African (SSA) cities. However, limited city-wide data constitutes a major barrier to investigating health impacts as well as implementing environmental policy in this growing population. As such, in this first of its kind study in West Africa, we measured, modelled and predicted environmental noise across the Greater Accra Metropolitan Area (GAMA) in Ghana, and evaluated inequalities in exposures by socioeconomic factors. Specifically, we measured environmental noise at 146 locations with weekly (n = 136 locations) and yearlong monitoring (n = 10 locations). We combined these data with geospatial and meteorological predictor variables to develop high-resolution land use regression (LUR) models to predict annual average noise levels (LAeq24hr, Lden, Lday, Lnight). The final LUR models were selected with a forward stepwise procedure and performance was evaluated with cross-validation. We spatially joined model predictions with national census data to estimate population levels of, and potential socioeconomic inequalities in, noise levels at the census enumeration-area level. Variables representing road-traffic and vegetation explained the most variation in noise levels at each site. Predicted day-evening-night (Lden) noise levels were highest in the city-center (Accra Metropolis) (median: 64.0 dBA) and near major roads (median: 68.5 dBA). In the Accra Metropolis, almost the entire population lived in areas where predicted Lden and night-time noise (Lnight) surpassed World Health Organization guidelines for road-traffic noise (Lden <53; and Lnight <45). The poorest areas in Accra also had significantly higher median Lden and Lnight compared with the wealthiest ones, with a difference of ∼5 dBA. The models can support environmental epidemiological studies, burden of disease assessments, and policies and interventions that address underlying causes of noise exposure inequalities within Accra.

Predicting traffic noise using land-use regression-a scalable approach

BACKGROUND

In modern societies, noise is ubiquitous. It is an annoyance and can have a negative impact on human health as well as on the environment. Despite increasing evidence of its negative impacts, spatial knowledge about noise distribution remains limited. Up to now, noise mapping is frequently inhibited by the necessary resources and therefore limited to selected areas.

OBJECTIVE

Based on the assumption, that prevalent noise is determined by the arrangement of sources and the surrounding environment in which the sound propagates, we build a geostatistical model representing these parameters. Aiming for a large-scale noise mapping approach, we utilize publicly available data, context-aware feature engineering and a linear land-use regression (LUR) model.

METHODS

Compliant to the European Noise Directive 2002/49/EG, we work at a high spatial granularity of 10 × 10-m resolution. As reference, we use the day-evening-night noise level indicator L_den. Therewith, we carry out 2000 virtual field campaigns simulating different sampling schemes and introduce spatial cross-validation concepts to test the transferability to new areas.

RESULTS

The experimental results suggest the necessity for more than 500 samples stratified over the different noise levels to produce a representative model. Eventually, using 21 selected variables, our model was able to explain large proportions of the yearly averaged road noise (L_den) variability (R² = 0.702) with a mean absolute error of 4.24 dB(A), 3.84 dB(A) for build-up areas, respectively. In applying this best performing model for an area-wide prediction, we spatially close the blank spots in existing noise maps with continuous noise levels for the entire range from 24 to 106 dB(A).

SIGNIFICANCE

This data is new, particular for small communities that have not been mapped sufficiently in Europe so far. In conjunction, our findings also supplement conventionally sampled studies using physical microphones and spatially blocked cross-validations.

Application of land use regression to map environmental noise in Shanghai, China

BACKGROUND

Urban environment noise has been linked with wide adverse effects on health; however, noise epidemiological researches are hindered by the lack of large-scale population-based exposure assessment.

OBJECTIVE

We aimed to measure noise levels over multiple seasons and to establish an LUR model to assess the spatial variability of intra-urban noise and identify its potential sources in Shanghai, China.

METHODS

Forty-minute (L_{Aeq, 40 min}) measurements of environmental noise were collected at 144 fixed sites, and each was visited three times (morning, afternoon, and evening) in winter, spring, and summer in 2019. Noise measurements were then integrated with land-use types, road networks, socioeconomic variables, and geographic information systems to construct LUR models. Ten-fold cross-validation was used to test the model performance.

RESULTS

A total of 1296 measurements and 29 predicting variables were used to estimate the spatial variation in environmental noise. The annual mean (±standard deviation) of L_{Aeq, 40min}, was 62 ± 8 dB (A). Significant variations were observed among monitoring sites but not between seasons or time of day. The LUR model explained 79% of the spatial variability of the noise, and the R² of the ten-fold cross-validation was 0.75. The most contributory predictors of noise level were road-related variables all within the 50-m buffers, followed by urban area within a 50-m buffer, total area of buildings within a 1000-m buffer, and number of restaurant clusters within a 50-m buffer. Farmland area within a 100-m buffer was the only negative variable in the model. A 50-m resolution noise prediction map was produced and suggested high noise level in urban areas and near traffic arteries.

CONCLUSION

LUR can be a robust method for reflecting noise variability in megacities such as Shanghai and may provide an efficient solution for noise exposure assessment in areas where noise maps are not available.

Predicting intraurban airborne PM1.0-trace elements in a port city: Land use regression by ordinary least squares and a machine learning algorithm

Airborne particulate matter (PM) has been associated with cardiovascular and respiratory morbidity and mortality, and there is some evidence that spatially varying metals found in PM may contribute to adverse health effects. We developed spatially refined models for PM trace elements using ordinary least squares land use regression (OLS-LUR) and machine leaning random forest land-use regression (RF-LUR). Two-week integrated measurements of PM_1.0 (median aerodiameter < 1.0 μm) were collected at 50 sampling sites during fall (2010), winter (2011), and summer (2011) in the Halifax Regional Municipality, Nova Scotia, Canada. PM_1.0 filters were analyzed for metals and trace elements using inductively coupled plasma-mass spectrometry. OLS- and RF-LUR models were developed for approximately 30 PM_1.0 trace elements in each season. Model predictors included industrial, commercial, and institutional/ government/ military land use, roadways, shipping, other transportation sources, and wind rose information. RF generated more accurate models than OLS for most trace elements based on 5-fold cross validation. On average, summer models had the highest cross validation R² (OLS-LUR = 0.40, RF-LUR = 0.46), while fall had the lowest (OLS-LUR = 0.27, RF-LUR = 0.31). Many OLS-LUR models displayed overprediction in the final exposure surface. In contrast, RF-LUR models did not exhibit overpredictions. Taking overpredictions and cross validation performances into account, OLS-LUR performed better than RF-LUR in roughly 20% of the seasonal trace element models. RF-LUR models provided more interpretable predictors in most cases. Seasonal predictors varied, likely due to differences in seasonal distribution of trace elements related to source activity, and meteorology.

The Development and Application of Machine Learning in Atmospheric Environment Studies

Machine learning (ML) plays an important role in atmospheric environment prediction, having been widely applied in atmospheric science with significant progress in algorithms and hardware. In this paper, we present a brief overview of the development of ML models as well as their application to atmospheric environment studies. ML model performance is then compared based on the main air pollutants (i.e., PM2.5, O3, and NO2) and model type. Moreover, we identify the key driving variables for ML models in predicting particulate matter (PM) pollutants by quantitative statistics. Additionally, a case study for wet nitrogen deposition estimation is carried out based on ML models. Finally, the prospects of ML for atmospheric prediction are discussed.

Tree characteristics and environmental noise in complex urban settings - A case study from Montreal, Canada

Field studies have shown that dense tree canopies and regular tree arrangements reduce noise from a point source. In urban areas, noise sources are multiple and tree arrangements are rarely dense. There is a lack of data on the association between the urban tree canopy characteristics and noise in complex urban settings. Our aim was to investigate the spatial variation of urban tree canopy characteristics, indices of vegetation abundance, and environmental noise levels. Using Light Detection and Ranging point cloud data for 2015, we extracted the characteristics of 1,272,069 public and private trees across the island of Montreal, Canada. We distinguished needle-leaf from broadleaf trees, and calculated the percentage of broadleaf trees, the total area of the crown footprint, the mean crown centroid height, and the mean volume of crowns of trees that were located within 100m, 250m, 500m, and 1000m buffers around 87 in situ noise measurement sites. A random forest model incorporating tree characteristics, the normalized difference vegetation index (NDVI) values, and the distances to major urban noise sources (highways, railways and roads) was employed to estimate variation in noise among measurement locations. We found decreasing trends in noise levels with increases in total area of the crown footprint and mean crown centroid height. The percentages of increased mean squared error of the regression models indicated that in 500m buffers the total area of the crown footprint (29.2%) and the mean crown centroid height (12.6%) had a stronger influence than NDVI (3.2%) in modeling noise levels; similar patterns of influence were observed using other buffers. Our findings suggest that municipal initiatives designed to reduce urban noise should account for tree features, and not just the number of trees or the overall amount of vegetation.

Land use regression modelling of community noise in São Paulo, Brazil

Noise pollution has negative health consequences, which becomes increasingly relevant with rapid urbanization. In low- and middle-income countries research on health effects of noise is hampered by scarce exposure data and noise maps. In this study, we developed land use regression (LUR) models to assess spatial variability of community noise in the Western Region of São Paulo, Brazil.We measured outdoor noise levels continuously at 42 homes once or twice for one week in the summer and the winter season. These measurements were integrated with various geographic information system variables to develop LUR models for predicting average A-weighted (dB(A)) day-evening-night equivalent sound levels (L_den) and night sound levels (L_night). A supervised mixed linear regression analysis was conducted to test potential noise predictors for various buffer sizes and distances between home and noise source. Noise exposure levels in the study area were high with a site average L_den of 69.3 dB(A) ranging from 60.3 to 82.3 dB(A), and a site average L_night of 59.9 dB(A) ranging from 50.7 to 76.6 dB(A). LUR models had a good fit with a R² of 0.56 for L_den and 0.63 for L_night in a leave-one-site-out cross validation. Main predictors of noise were the inverse distance to medium roads, count of educational facilities within a 400 m buffer, mean Normalized Difference Vegetation Index (NDVI) within a 100 m buffer, residential areas within a 50 m (L_den) or 25 m (L_night) buffer and slum areas within a 400 m buffer. Our study suggests that LUR modelling with geographic predictor data is a promising and efficient approach for noise exposure assessment in low- and middle-income countries, where noise maps are not available.

Statistical Road-Traffic Noise Mapping Based on Elementary Urban Forms in Two Cities of South Korea

Statistical models that can generate a road-traffic noise map for a city or area where only elementary urban design factors are determined, and where no concrete urban morphology, including buildings and roads, is given, can provide basic but essential information for developing a quiet and sustainable city. Long-term cost-effective measures for a quiet urban area can be considered at early city planning stages by using the statistical road-traffic noise map. An artificial neural network (ANN) and an ordinary least squares (OLS) model were developed by utilizing data on urban form indicators, based on a 3D urban model and road-traffic noise levels from a normal noise map of city A (Gwangju). The developed ANN and OLS models were applied to city B (Cheongju), and the resultant statistical noise map of city B was compared to an existing normal road-traffic noise map of city B. The urban form indicators that showed multi-collinearity were excluded by the OLS model, and among the remaining urban forms, road-related urban form indicators such as traffic volume and road area density were found to be important variables to predict the road-traffic noise level and to design a quiet city. Comparisons of the statistical ANN and OLS noise maps with the normal noise map showed that the OLS model tends to under-estimate road-traffic noise levels, and the ANN model tends to over-estimate them.

[Satellite data for recording health-relevant environmental conditions: examples and interdisciplinary potential]

Environmental conditions influence human health and interact with other factors such as DNA, lifestyle, or the social environment. Earth observations from space provide data on the most diverse manifestations of these environmental conditions and make it possible to quantify them spatially. Using two examples - the availability of open and recreational space and the spatial distribution of air pollution - this article presents the potential of Earth observations for health studies. In addition, possible applications for health-related issues are discussed. To this end, we try to outline key points for an interdisciplinary approach that meets the conceptual, data technology, and ethical challenges.