Ambient fine particulate matter (PM_2.5) pollution is a major health risk. Networks of low-cost sensors (LCS) are increasingly being used to understand local-scale air pollution variation. However, measurements from LCS have uncertainties that can act as a potential barrier to effective decision making. LCS data thus need adequate calibration to obtain good quality PM_2.5 estimates. In order to develop calibration factors, one or more LCS are typically co-located with reference monitors for short or long periods of time. A calibration model is then developed that characterizes the relationships between the raw output of the LCS and measurements from the reference monitors. This calibration model is then typically transferred from the co-located sensors to other sensors in the network. Calibration models tend to be evaluated based on their performance only at co-location sites. It is often implicitly assumed that the conditions at the relatively sparse co-location sites are representative of the LCS network overall and that the calibration model developed is not overfitted to the co-location sites. Little work has explicitly evaluated how transferable calibration models developed at co-location sites are to the rest of an LCS network, even after appropriate cross-validation. Further, few studies have evaluated the sensitivity of key LCS use cases, such as hotspot detection, to the calibration model applied. Finally, there has been a dearth of research on how the duration of co-location (short-term or long-term) can impact these results. This paper attempts to fill these gaps using data from a dense network of LCS monitors in Denver deployed through the city's “Love My Air” program. It offers a series of transferability metrics for calibration models that can be used in other LCS networks and some suggestions as to which calibration model would be most useful for achieving different end goals.

A total of 20 urban neighbourhood-scale eddy covariance flux tower datasets are made openly available after being harmonized to create a 50 site–year collection with broad diversity in climate and urban surface characteristics. Variables needed as inputs for land surface models (incoming radiation, temperature, humidity, air pressure, wind and precipitation) are quality controlled, gap-filled and prepended with 10 years of reanalysis-derived local data, enabling an extended spin up to equilibrate models with local climate conditions. For both gap filling and spin up, ERA5 reanalysis meteorological data are bias corrected using tower-based observations, accounting for diurnal, seasonal and local urban effects not modelled in ERA5. The bias correction methods developed perform well compared to methods used in other datasets (e.g. WFDE5 or FLUXNET2015). Other variables (turbulent and upwelling radiation fluxes) are harmonized and quality controlled without gap filling. Site description metadata include local land cover fractions (buildings, roads, trees, grass etc.), building height and morphology, aerodynamic roughness estimates, population density and satellite imagery. This open collection can help extend our understanding of urban environmental processes through observational synthesis studies or in the evaluation of land surface environmental models in a wide range of urban settings. These data can be accessed from https://doi.org/10.5281/zenodo.7104984 (Lipson et al., 2022).

Extreme air quality episodes represent a major threat to human health worldwide but are highly dynamic and exceedingly challenging to monitor. The 2018 Kīlauea Lower East Rift Zone eruption (May to August 2018) blanketed much of Hawai‘i Island in “vog” (volcanic smog), a mixture of primary volcanic sulfur dioxide (SO₂) gas and secondary particulate matter (PM). This episode was captured by several monitoring platforms, including a low-cost sensor (LCS) network consisting of 30 nodes designed and deployed specifically to monitor PM and SO₂ during the event. Downwind of the eruption, network stations measured peak hourly PM_2.5 and SO₂ concentrations that exceeded 75 μg m⁻³ and 1,200 parts per billion (ppb), respectively. The LCS network’s high spatial density enabled highly granular estimates of human exposure to both pollutants during the eruption, which was not possible using preexisting air quality measurements. Because of overlaps in population distribution and plume dynamics, a much larger proportion of the island’s population was exposed to elevated levels of fine PM than to SO₂. Additionally, the spatially distributed network was able to resolve the volcanic plume’s chemical evolution downwind of the eruption. Measurements find a mean SO₂ conversion time of ∼36 h, demonstrating the ability of distributed LCS networks to observe reaction kinetics and quantify chemical transformations of air pollutants in a real-world setting. This work also highlights the utility of LCS networks for emergency response during extreme episodes to complement existing air quality monitoring approaches. Significance Poor air quality is a global public health issue, contributing to millions of premature deaths per year worldwide. Low-cost air quality sensors are a promising tool to improve monitoring capabilities. In this study, we built and deployed a low-cost sensor network for emergency response during an extreme air quality event, the 2018 Kīlauea Lower East Rift Zone eruption. This network was used to estimate fine-scale population exposures to multiple pollutants, to measure the chemical transformation of volcanic emissions, and to provide real-time observations as part of emergency management efforts.

Sensible heat flux (Q_H) is a critical driver of surface and boundary layer meteorological processes, especially in urban areas. Aerodynamic resistance methods (ARM) for modelling Q_H are promising because, in principle, all that is needed is surface temperature (T₀), air temperature (T_A) and an aerodynamic resistance term (r_H). There are significant challenges in urban areas, however, due to uncertainties in satellite‐derived land surface temperatures (LST), logistical challenges in obtaining high‐resolution air temperatures, and a limited understanding of spatial and temporal variability of r_H and associated variables (e.g. thermal roughness length). This work uses an extensive LST dataset covering 6 years (2011–2016) in central London and a long‐term in situ observation network to analyse the variability of LST and r_H variables. Results show that LST is spatially correlated with building and vegetation land cover with coherent thermal structures at length scales less than 500–1,000 m. Additionally, satellite‐observed LST varies with average building height (up to 10% cooler in areas with tall buildings). The r_H term and associated variables are observed to vary on daily and seasonal cycles, and findings are used to model Q_H using five variations of an ARM‐based approach on a 100 m pixel basis. Modelled Q_H is compared to observations from three scintillometer paths and an eddy covariance flux tower. We find generally good agreement between observations and models, although there is uncertainty in all methods (mean absolute error ranges from 58.1–129.3 W/m²) due to the challenges involved in determining high‐resolution meteorological and surface inputs, particularly LST and friction velocity (u_*). Additional complexity in evaluating modelled Q_H arises from anthropogenic heat sources: long‐term tower‐based observations show that T_A and the radiometer‐derived T₀ are warmer during working weekdays than non‐working days (up to 0.7 °C) and that there is an observed lag (2–3 hr) between energy consumption and the observed warming and modelled Q_H.

Focusing on commercial office real estate as both a manifestation of and a conduit of cross-border capital flows, this paper refers to the concepts of topology and topography in a theoretical and empirical exploration of contemporary ‘network economy’ spatial implications for the ‘polycentric urban region’ (PUR). A body of research has cast doubt on the normative European representation of the multi-centre PUR as a balanced, sustainable spatial development model. Yet, the model has continued to be propagated in European territorial strategy and has been influential internationally. Academic perspectives and qualitative evidence reviewed in the paper shed a light on mutual dependencies and recursive relations between network economy global structural processes, international office real estate investment practices mediated by city governments, and the spatial configuration of density. Commercial investment and city planning actor practices chime with urban agglomeration, spatial concentration and density. Quantitative evidence of associations between urban density and office real estate investment returns and capital flows is found. It is concluded that network economy topology, politics and the city are in a dialectical relationship with the PUR territorial governance agenda for spatially balanced regional development.

Urban vegetation is growing in importance as cities use ‘green infrastructure’ to mitigate the impacts of climate change, reduce extreme heat, and improve human health and comfort. However, due to the heterogeneity of city landscapes, urban vegetation experiences a diverse range of environmental conditions, potentially leading to differences in growing season timing and length within cities. Here, we investigate physical drivers of urban land surface phenology and timing within a semi-arid city (Denver, CO, USA) using four years (2018–2021) of remotely sensed vegetation indices, modelled air temperature, and land cover datasets. Within the metropolitan region study area, satellite-based vegetation index measurements indicate that growing season length is variable on sub-neighborhood spatial scales. This variability is largely due to differences in the timing of fall senescence, as opposed to early season growth. Areas with substantial fractions of irrigated land cover tend to remain greener for longer, while unirrigated and cooler areas are correlated with an earlier end to the growing season (up to ∼two months shorter). These findings complement those from non-arid cities where surface and air temperature are the dominant environmental control on phenological timing. Results here indicate the importance of soil moisture for phenology in semi-arid regions and suggest unique semi-arid urban growing season dynamics and temperature-vegetation feedbacks. These interactions have implications for water, heat, and vegetation management strategies to maximize ecosystem services in water-limited environments.