This study presents a comprehensive global assessment of the runoff retention performance of green roofs. These infrastructures, also identified as vegetated roofs, eco roofs or ecological roofs, are widely acknowledge for their capacity to retain rainwater. However, the effect of their drivers remains insufficiently understood. Moreover, reliable and easy-to-use tools for estimating retention performance under limited data availability and across diverse climatic contexts are still lacking. Using a worldwide dataset comprising 2692 samples, this systematic review assesses the runoff retention performance of green roofs through two complementary modelling approaches: statistical analysis using General Linear Models (GLM), and machine-learning methods using Multivariate Adaptive Regression Splines (MARS). Results indicate that rainfall, climate, location, vegetation, substrate and drainage layer influence runoff retention. The developed models predict runoff with correlation factors varying between 0.92 and 0.98. Compared with GLM, MARS models select a reduced set of predictors and enable the identification of nonlinear and interaction effects. In particular, interactions are identified between rainfall depth and climate, substrate depth and drainage layer type, and between climate and location, while the effect of rainfall depth on retention is shown to be nonlinear. Overall, the proposed modelling framework provides insights into the drivers of green roof hydrological performance and offers a practical screening tool to inform preliminary stormwater design and planning decisions, particularly where detailed physical data are unavailable.

The use of green infrastructure (GI) in urban environments has been widely investigated for its associated ecosystem services including air pollution mitigation. Plants are well-known for their ability of purifying air through photosynthesis and microbial activities occurring in the rhizosphere, however the simple filtration of particulate matter in air by plants is still not well understood. This study aimed to investigate the potential to adapt classic filtration theory for application in GI design. Two native Australian plants used as filter media were involved in laboratory experiments to remove aerosol particles ranging in size from 0.3 to > 10 µm. A comparison of aerosol removal efficiencies obtained from the laboratory experiments and predicted through classic filtration theory showed good correlation for the smaller (needle-like) leaf system. In contrast, the correlation obtained for a plant with larger elliptical leaves was not as good, showing a larger difference between the results. Such outcomes led to the conclusion that smaller and spatially homogeneous plant systems have more predictable aerosol filtration characteristics, which can be reasonably calculated using filtration theory. This finding provides practical insight into GI design, allowing quantitative predictions of local air pollution reductions using green barriers.

Direct seeding is increasingly recognised as a practical method for ecological restoration; however, its application in post-mining rehabilitation remains limited due to concerns regarding establishment reliability, species performance, and cost-effectiveness. This study evaluates the effectiveness of direct seeding for mine rehabilitation at an operational sand mine in Southeast Queensland, with a focus on achieving compliance with Queensland's Progressive Rehabilitation and Closure Plan (PRCP) requirements. A seed mix comprising 10 native woody species-including primary and secondary koala food trees (Eucalyptus and Corymbia spp.)-and five native grass species was applied across two sites with contrasting land-use histories. Vegetation establishment was monitored over a 36-month period, with performance assessed in terms of species richness, plant density, and the influence of non-native vegetation. At the Native Vegetation Site, canopy tree densities substantially exceeded PRCP targets (5200 vs. 600 stems/ ha), and key koala food trees also surpassed their respective benchmarks. In contrast, native grass establishment was lower than anticipated, potentially reflecting dormancy constraints, site-specific soil properties, and competition with exotic species. At the Pasture Site, the dominance of Chloris gayana (Rhodes grass) significantly inhibited native plant recruitment, highlighting the importance of targeted weed management. A cost analysis indicated that direct seeding was approximately 30 % more cost-effective than tubestock planting, while supporting greater species and structural diversity. These findings demonstrate that, when implemented with appropriate site preparation and management, direct seeding represents a scalable, economically viable, and ecologically robust strategy for post-mining landscape rehabilitation.

Background: Air pollution from the 2023 Quebec wildfires affected New York state (NY) with daily average PM2.5 levels that peak on June 7. Increased Covid-19 hospitalizations were recorded weeks after the wildfires. This study analyses the trend of Covid-19 hospitalization in NY counties after the 2023 Quebec wildfires and estimates their association with higher PM2.5 concentration levels, compared to 2022.Design and methods: A Bayesian spatiotemporal regression model was used to estimate the impact of wildfire smoke on Covid-19 hospitalizations. Four periods of pre/post-wildfire and 7-day post-wildfire daily hospitalization periods were considered to compare the association of daily average PM2.5 levels, from May 1 to June 7, with daily Covid-19 hospitalization rates in NY counties in 2022 and 2023. The pre/post-wildfire and 7-day post-wildfire periods considered a lag of 2, 4, 6, and 8 weeks and 24, 48, 72, and 96 h, respectively. The model was adjusted for sociodemographic factors.Results: The Covid-19 hospitalization rate followed an increasing trend in the second, third and fourth pre/post-wildfire periods in 2023 in contrast with 2022 when no trends were identified. Each PM2.5 unit increase was associated with a 2%; 6% and 7% Covid-19 higher hospitalization risk in periods 2, 3, and 4, respectively, in 2023 only. These findings identify a potential impact of wildfire smoke on the severity of Covid-19 morbidity after 2 weeks of the wildfires. Robust spatiotemporal analyses can be used to identify specific at-risk areas and communities to support public health decision-making and health strategies.Conclusions: This study identifies a higher risk of Covid-19 hospitalization in New York State associated with higher air pollution levels from the 2023 Quebec wildfires, in the first week and 2, 3, and 4 weeks after the wildfires. These findings concur with the increasingly investigated association of air pollution with severe Covid-19. The methodological approach of this study shows the utility of spatiotemporal epidemiological analyses and need for future research on wildfire smoke as a potential determinant of severe Covid-19. With more frequent and extreme climate events it is paramount to improve our understanding of many potential health impacts of wildfires to prepare strategies to deal with, and potentially anticipate, environmental health and healthcare responses in wildfire-prone regions.

Glyphosate is widely used in horticultural land management practices, but its environmental risks, especially in biochar-amended soil, remain poorly understood. This study aimed to evaluate the effects of repeated glyphosate applications on biochar-amended- soils, focusing on changes in soil nitrogen (N) cycling, soil microbial diversity, and community structure. The experiment was conducted in a macadamia orchard where wood-based biochar had been applied for five years prior to our study. Simultaneously, glyphosate (Roundup (R)) had been applied at the recommended label rate (4 L ha- 1) with active ingredient of glyphosate of 360 g L-1, in a strip under tree canopy for 12 years, until its use was stopped two years prior to our sample collection. Thus, biochar and glyphosate were applied concurrently for three years before glyphosate use was stopped. Soil samples were collected from three areas, including: under the tree canopy with and without a history of glyphosate application; and areas outside the tree canopy with no history of glyphosate application. Changes in foliar and soil total carbon (TC), total nitrogen (TN) and soil N isotope composition (S15N), and microbial community composition, were subsequently assessed. Our findings revealed that soil TN and S15N were significantly higher under tree canopy with a history of glyphosate application compared with areas without glyphosate application history likely due to the die back of weed because the other sections were mechanically managed. Glyphosate residues were also found under and outside tree canopy where no glyphosate was applied. Therefore, higher TN and S15N under tree canopy could not directly be attributed to glyphosate application. Overall, neither glyphosate nor biochar influenced the soil microbial diversity and community structure. This study suggested that glyphosate application and farm management practices may have long-term implications for soil N cycling, even after application has stopped.

The presence of organic micropollutants (OMPs) in wastewater poses a growing environmental risk, particularly for aquatic ecosystems and water reuse efforts. This study evaluates UV/peracetic acid (UV/PAA) oxidation combined with biofiltration as a sustainable solution for OMP degradation and toxicity mitigation. Over a sixmonth continuous bench scale study, secondary effluent was treated with UV/PAA or conventional UV/H2O2, followed by biological activated carbon (BAC) filtration. Treatment performance was assessed using targeted chemical analysis of common OMPs alongside effect-based bioassays (estrogenicity, cytotoxicity, phytotoxicity, and oxidative stress). Results demonstrated that UV/PAA pretreatment outperformed UV/H2O2in degrading recalcitrant OMPs, achieving notable removal efficiencies for carbamazepine (65 %) and diuron (52 %). Additionally, UV/PAA significantly reduced photosynthesis inhibition (53 %) compared to UV/H2O2(21 %). While the combined UV/PAA-biofiltration process did not further lower photosynthesis inhibition, it achieved a 63 % reduction in algal toxicity and over 60 % reduction in estrogenic load, effectively lowering the estrogenicity risk. Overall, biofiltration played a key role in mitigating most toxicity endpoints. Microbial community analysis revealed that biofilters supported the growth of OMP-degrading bacteria, including Pseudomonas, Mycobacterium, and Rhodococcus. UV/PAA pretreatment enhanced the abundance of Acinetobacter and Anaerolinea, species known for degrading estrogens and other OMPs. Cost analysis indicated higher operational costs for UV/PAA ($5.88 USD/m3) than UV/H2O2($2.36 USD/m3), but UV/PAA demonstrated better cost-effectiveness per unit of OMP removal. The study underscores UV/PAA-biofiltration as a promising but cost-sensitive alternative to conventional UVAOPs, with key benefits in toxicity mitigation and retrofit potential.

Street and park trees often endure harsher conditions, including increased temperatures and drier soil and air, than those found in urban or natural forests. These conditions can lead to shorter lifespans and a greater vulnerability to dieback. This literature review aimed to identify confirmed causes of street and park tree dieback in urban areas from around the world. Peer-reviewed case studies related to urban tree decline were scanned for the words "urban", "city", "cities", "tree*", "decline", "dieback", "mortality", and "survival". From an initial pool of 1281 papers on Web of Science and 1489 on Scopus, 65 original peer-reviewed research papers were selected for detailed analysis. Out of all species reported to decline, 46 were native, while non-natives were represented by 35 species. The most commonly affected trees were Platanus, Fraxinus, Acer, and Ficus. Most studies were conducted in Mediterranean, humid subtropical, and humid continental climates, with the greatest representation from the United States, followed by Australia, Brazil, Iran, Italy, and Russia. Many authors focused on either biotic or abiotic causes of dieback; some explored both, and some also discussed underlying environmental and urban stresses as potential predisposing factors. The majority (81% of the papers) concluded that a decline was caused by either an arthropod or a microorganism. Overall, it was suggested that changing management strategies to improve water availability and soil health might help with tree resilience. Additionally, regular monitoring and research, along with improving tree species selection and implementing biological and chemical control methods, can help prevent or slow down tree decline. Increasing awareness and adopting preventative approaches could help to extend the lifespan of street and park trees in urban environments and mitigate some of the biological threats, especially considering the challenges we may be facing due to the changing climate.

Australia is rich in minerals of commercial interest along with oil and gas, and mining activities are carried out in almost all states and territories. The public health impacts of mining on the Australian general population need to be addressed to enable a comprehensive cost-benefit assessment of these activities balanced against their broader impacts. This systematic search and thematic review of the literature evidenced that exposure to agents released during mining operations, such as cadmium, iron, manganese, zinc, arsenic and lead, is associated with neoplastic and non-neoplastic diseases in adults and children. Mining of lead is specifically associated with negative fertility effects in men and with intellectual disability and impaired immune function in children. Asbestos mining is associated with higher morbidity and mortality due to respiratory and non-respiratory cancers, and recent analyses have identified a higher risk of severe respiratory and circulatory diseases in communities in proximity to coal mining. Although unconventional gas extraction is more newly introduced in Australia, research has found a higher risk of hospitalisation by all-causes and for circulatory, respiratory and blood and immune diseases, especially in children. These findings are consistent with extensive research globally, but human studies in this field are scarce in Australia. Multisectoral approaches are required to address these impacts, including committed involvement of the mining industry, the academic sector and, especially, the different levels of government.

The growing interest in utilizing recycled waste substrates (RWS) in ecosystem services and environmental remediation aligns with the "waste to wealth" concept and the Sustainable Development Goals (SDGs). Despite the promising potential of RWS, research gaps remain due to a lack of comprehensive reviews on their production and applications. This systematic review attempts to synthesize and critically assess the scientific footprint of RWS through robust methodology and thorough investigation. Characterization of scientific literature, network analysis, and systematic review were conducted on articles indexed in the Web of Science and Scopus databases. Quantitative and qualitative analyses were performed on 140 articles selected by the rigorous article screening process executed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. The findings map the scientific literature and research themes in RWS. Around 66 % of studies in RWS used a multiple research approach, primarily experiments with case studies. Key research topics identified include (A) Technical domains - types of wastes and recycling techniques in RWS production and parameters influencing the substrate quality; (B) Application domains: environmental remediation of soil and agriculture and horticulture. The use of RWS in urban green infrastructure, particularly for green roofs and vegetative walls, and the potential for LCA studies on RWS production and applications emerge as promising areas for future research. This systematic review also presents a conceptual framework model (CFM) on RWS research, encapsulating the state-of-the-art themes, risks, limitations and constraints, and future research avenues.

Wildflower meadows are a low-maintenance landscape treatment that can improve urban biodiversity and achieve conservation outcomes, especially when designed to use plants from threatened grassy ecosystems. Cost-effective approaches to create meadows include direct seeding onto mined sand substrates that are placed onto site soils to supress weed competition and enhance sown plant establishment. However, waste subsoils diverted from landfill could provide a more sustainable alternative. This study compares a mined sand with a clay subsoil to understand the relative differences in sown plant establishment and root growth for a range of south-east Australian grassland species. Germination, seedling emergence and root development were assessed for six species sown in an 80 mm deep cap of two low nutrient substrates (sand and clay subsoil) overlying a simulated site soil. Rhizoboxes were used to assess the rate at which plant roots could access soils beneath capping substrates. Sand and clay subsoil supported the establishment of the six sown species. Five species had significantly greater total root length, leaf area and aboveground biomass when sown in recycled subsoil, compared with sand. Edaphic conditions affect the establishment of native grassland species in wildflower meadows. Compared to sand, recycled clay subsoils provide a more sustainable alternative for establishing designed wildflower meadows and can support rapid root and plant growth in south-east Australian grassland species.

Purpose Particle size distribution (PSD) assessment, which affects all physical, chemical, biological, mineralogical, and geological properties of soil, is crucial for maintaining soil sustainability. It plays a vital role in ensuring appropriate land use, fertilizer management, crop selection, and conservation practices, especially in fragile soils such as those of the North-Western Himalayas.Materials and methods In this study, the performance of eleven mathematical and three Machine Learning (ML) models used in the past was compared to investigate PSD modeling of different soils from the North-Western Himalayan region, considering that an appropriate model must fit all PSD data.Results and discussion Our study focuses on the significance of evaluating the goodness of fit in particle size distribution modeling using the coefficient of determination (R2 adj = 0.79 to 0.45), the Akaike information criterion (AIC = 67 to 184), and the root mean square error (RMSE = 0.01 to 0.09). The Fredlund, Weibull, and Rosin Rammler models exhibited the best fit for all samples, while the performance of the Gompertz, S-Curve, and Van Genutchen models was poor. Of the three ML models tested, the Random Forest model performed the best (R2 = 0.99), and the SVM model was the lowest (R2 = 0.95). Thus, the PSD of the soil can be best predicted by ML approaches, especially by the Random Forest model.Conclusion The Fredlund model exhibited the best fit among mathematical models while random forest performed best among the machine learning models. As the number of parameters in the model increased better was the accuracy.

Closed landfills represent a large portion of land that cannot be developed but can potentially be ecologically rehabilitated using capping technologies such as phytocapping. This study aims to identify and quantify the value of landfills for ecological rehabilitation in Queensland, Australia, using GIS (Geographic Information Systems) and multi-criteria analysis. Multiple environmental features were used for this study and were selected to represent ecosystem services and connectivity. These features were used to create two novel scores (environmental and habitat score), representing the total area and number of environmental features. The evaluation shows there is high value for ecological rehabilitation of landfills in South East Queensland and the Wet Tropics, signifying the importance of ecological rehabilitation in these biogeographic regions. Landfill rehabilitation can create high-value habitats for endangered species and enhance connectivity. It is recommended that landfill after-use is considered by regulators and stakeholders as a potential vehicle for achieving environmental sustainability and ecological conservation. The developed methodology in this study can be used to inform the decision-making process for landfill after-use and the strategic prioritisation of landfills within local governmental areas for achieving ecological rehabilitation.

Soil density or compaction is one of the key variables that need to be specified for designed soil profiles as part of urban greening. Examples include land rehabilitation (mining), waste containment (phytocapping), stormwater infrastructure (bioretention basins) and green infrastructure (green roofs and street trees). This study investigates the impact of a range of specified soil densities on plant water use and root development. Native Australian plant species selected for the study include: the C4 and C3 grasses, Themeda triandra and Microlaena stipoides respectively; the Eucalyptus trees, E. camaldulensis and E. cladocalyx; and the nitrogen-fixing pioneer trees, Acacia mearnsii and Allocasuarina verticillata. The plants were established at four soil density (compaction) levels: 72, 77, 82, and 87 % MDD (maximum dry density) in tall cylinders with weekly plant water use measurement over 8 months. Root growth was analysed using WinRhizo image analysis. The experimental results were used to create generalisable models for root length density (RLD), root diameter and plant water use. The models for RLD and plant water use were parabolic in nature, revealing clear optimum ranges that could be used to guide soil density specification. Root diameter provided additional insight into the allocation of resources to root thickening above a threshold soil density of 87 % MDD, indicating plant allocation of resources towards penetrating highly compacted soils. There were correlations between plant water use and RLD that were moderate and significant, particularly for grasses. Notably, T. triandra had the greatest mean RLD, thickest roots and plant water use at 16.8cm/cm3, 0.18 mm and 10mm/week, respectively. Findings demonstrate that RLD and plant water use can be optimised together within practically achievable soil density specification ranges that are sensitive to the pitfalls of both excessively low and excessively high soil densities. Recommended soil density specification ranges include: 75–82 % MDD with plant performance within 5 % of optimum (considered excellent), 74–84 % MDD with plant performance within 10 % of optimum (considered good) and 73–85 % MDD with plant performance within 15 % of optimum (considered fair). Within these ranges, plant water use and root growth performance is well balanced with practical achievement of the soil density ranges. Due to the use of %MDD, the modelled results can be usefully generalised for any soil type. Implementation of these specifications for urban greening and phytocap projects will optimise plant growth, transpiration and hydrological function while maintaining root networks essential for establishing and maintaining resilient living infrastructure.

Biofiltration utilizes natural mechanisms including biodegradation and biotransformation along with other physical processes for the removal of organic micropollutants (OMPs) such as pharmaceuticals, personal care products, pesticides and industrial compounds found in (waste)water. In this systematic review, a total of 120 biofiltration studies from 25 countries were analyzed, considering various biofilter configurations, source water types, biofilter media and scales of operation. The study also provides a bibliometric analysis to identify the emerging research trends in the field. The results show that granular activated carbon (GAC) either alone or in combination with another biofiltration media can remove a broad range of OMPs efficiently. The impact of pre-oxidation on biofilter performance was investigated, revealing that pre-oxidation significantly improved OMP removal and reduced the empty bed contact time (EBCT) needed to achieve a consistently high OMP. Biofiltration with pre-oxidation had median removals ranging between 65% and >90% for various OMPs at 10-45 min EBCT with data variability drastically reducing beyond 20 min EBCT. Biofiltration without pre-oxidation had lower median removals with greater variability. The results demonstrate that pre-oxidation greatly enhances the removal of adsorptive and poorly biodegradable OMPs, while its impact on other OMPs varies. Only 19% of studies we reviewed included toxicity testing of treated effluent, and even fewer measured transformation products. Several studies have previously reported an increase in effluent toxicity because of oxidation, although it was successfully abated by subsequent biofiltration in most cases. Therefore, the efficacy of biofiltration treatment should be assessed by integrating toxicity testing into the assessment of overall removal.

The use of percent frequency-dependent magnetic susceptibility (χfd

Purpose Revegetation of riparian zones is important to improve their soil nitrogen (N) dynamics and to preserve their microbial compositions. However, the success of revegetation projects currently depends on weed control to reduce non-target vegetation competing over nutrients and to ensure the target plant species growth and survival. Different weed control methods affect soil microbial composition and N cycling. However, the long-term effects of herbicides on soil nitrogen (N) pools and microbial community composition remain uncertain even after cessation of the herbicide application. Materials and methods This study compared the impacts of different herbicides (Roundup ® , BioWeed™, Slasher ® , and acetic acid) with mulch on soil N dynamics and microbial community structure 3 years after vegetation establishment (herbicides applied repeatedly in the first 2 years after which no herbicides were applied in the third final year). Results and discussion Soil microbial biomass carbon (MBC) was significantly higher in mulch compared with Roundup ® , BioWeed™, Slasher ® , and acetic acid at month 26 at the Kandanga site and month 10 at the Pinbarren site. Soil MBC remained significantly higher in mulch compared with Roundup ® and BioWeed™, 12 months after the cessation of herbicide application at the Pinbarren site. Soil MBC in the Roundup ® and BioWeed™ groups was also lower than the acceptable threshold (160 mg kg −1 ) at month 34 at the Pinbarren site. Soil NO 3 − -N was significantly higher in the mulch than the Roundup ® at months 22 and 34 after revegetation at the Pinbarren site which could be partly explained by the decreased abundance of the denitrifying bacteria ( Candidatus solibacter and C. koribacter ). Additionally, both soil bacterial and fungal communities at the Pinbarren site and only fungal community at the Kandanga site were different in the mulch group compared with all other herbicides. The differences persisted 12 months after the cessation of herbicide application at the Pinbarren site. Conclusion Our study suggested that the application of mulch to assist with riparian revegetation would be beneficial for soil microbial functionality. The use of herbicides may have long-lasting effects on soil microbial biomass and diversity and therefore herbicides should be used with caution as part of an integrated land management plan.

Morbidity statistics can be reported as grouped data for health services rather than for individual residence area, especially in low-middle income countries. Although such reports can support some evidence-based decisions, these are of limited use if the geographical distribution of morbidity cannot be identified. This study estimates the spatial rate of Acute respiratory infections (ARI) in census districts in Cúcuta -Colombia, using an analysis of the spatial distribution of health services providers. The spatial scope (geographical area of influence) of each health service was established from their spatial distribution and the population covered. Three levels of spatial aggregation were established considering the spatial scope of primary, intermediate and tertiary health services providers. The ARI cases per census district were then calculated and mapped using the distribution of cases per health services provider and the proportion of population per district in each level respectively. Hotspots of risk were identified using the Local Moran’s I statistic. There were 98 health services providers that attended 8994, 18,450 and 91,025 ARI cases in spatial levels 1, 2 and 3, respectively. Higher spatial rates of ARI were found in districts in central south; northwest and northeast; and southwest Cúcuta with hotspots of risk found in central and central south and west and northwest Cucuta. The method used allowed overcoming the limitations of health data lacking area of residence information to implementing epidemiological analyses to identify at risk communities. This methodology can be used in socioeconomic contexts where geographic identifiers are not attached to health statistics.

Background. Air pollution from the 2023 Quebec wildfires affected the New York (NY) state with daily average PM2.5 levels that peak on June 7. This was followed by an increase of Covid-19 hospitalizations several weeks after the wildfires. Previous research has estimated an association between wildfire smoke and Covid-19 in wildfire prone regions, with PM2.5 levels used as an indicator of wildfire air pollution. Although identifying the potential causal impacts of wildfire smoke on severe Covid-19 morbidity requires analyses of individual level data, exploratory analyses of spatially aggregated data can provide important insights of the disease trend and the specific areas at risk to support public health responses. This study analyses the trend of Covid-19 hospitalizations in NY in the weeks following the 2023 Quebec wildfires and explores its statistical association with the higher PM2.5 concentration levels, in comparison with 2022. Methods. A Bayesian spatiotemporal regression model was implemented in R with the R-INLA package to estimate the association of daily average PM2.5 levels with the daily Covid-19 hospitalization rate in the 62 NY counties and compare it with the year 2022. A 38-day period, from May 1 (1 month before the start of the 2023 Quebec wildfires) to June 7 (peak PM2.5 levels) was considered for the wildfire exposure. Four 38-day lag periods for 2, 4, 6, and 8 weeks after the start of the wildfires were considered to assess the potential impact of wildfire smoke on severe Covid-19 in the weeks following the wildfires. A similar analysis was implemented for the same periods in 2022. The models were adjusted for demographic factors and socioeconomic and environmental factors using the County Health Rankings. The county-specific risk estimated in the model was used to map the Covid-19 hospitalization risk in each of the four periods in both years 2022 and 2023. Results. There was a positive trend in the Covid-19 hospitalization in the second, third and fourth 38-day lag periods in 2023 in contrast with 2022 when no trends were identified. Higher PM2.5 levels were associated with a higher Covid-19 hospitalization rate in periods 2, 3 and 4 in 2023 with a negative association between PM2.5 levels and Covid-19 hospitalizations in period 1 in 2023. In 2023, each PM2.5 unit increase was associated with a 2%; 6% and 7% Covid-19 hospitalization risk in periods 2, 3 and 4, respectively. A positive association of the PM2.5 level with the Covid-19 hospitalization rate was no found in any of the four periods in 2022. There were clusters of higher Covid-19 hospitalization risk in NY counties in the southeast and southwest regions in periods 3 and 4. Most of the counties with the highest risk in 2023 coincided with daily average PM2.5 levels greater than the US-EPA standards.Conclusions. There is a statistical association between higher levels of PM2.5 in NY state after the 2023 Quebec wildfires with an increased risk of Covid-19 hospitalizations, 4 or more weeks after the start of the wildfires compared with the same period in 2022. Spatiotemporal regression analyses are helpful for rapid assessment of the potential impacts of wildfire air pollution on infectious and respiratory diseases such as Covid-19, to support public health decision-making. The statistical associations found in this study do not inform a causal relationship between wildfires air pollution and severe Covid-19 and further research with individual data is required to confirm any potential causal links.

Evolutionary design (ED) is a strategy that makes use of computational power to couple generative techniques with evaluation methods, to put forward designs that are better with each iteration. In this research, we present a representation scheme for solving spatial layout problems that is simple to implement as well as extend. The mechanisms for evaluation and mutation are defined and also shown to be extendable. Ultimately, the topic explored here is the ways in which ED and computation can enhance our design thinking and how computers can provide the background to new design processes and workflows.