Station Site Research Articles

Suitable Site Selection of Public Charging Stations: A Fuzzy TOPSIS MCDA Framework on Capacity Substation Assessment

The number of electric vehicles (EVs) continues to increase in the automobile market, driven by public policies since they contribute to the global decarbonization of the transportation sector. Still, the main challenge to increasing EV adoption is charging infrastructure. Therefore, the site selection of public EV charging stations should be made very carefully to maximize EV usage and address the population’s range anxiety. Since electricity demand for charging EVs introduces new load shapes, the interrelationship between the location of charging stations and long-term electrical grid planning must be addressed. The selection of the most suitable site involves conflicting criteria, requiring the application of multi-criteria analysis. Thus, a geographic information system-based Multicriteria Decision Analysis (MCDA) approach is applied in this work to address the charging station site selection, where the demographic criteria and energy density are taken into account to formulate an EV increase model. Several methods, including Fuzzy TOPSIS, are applied to validate the selection of suitable sites. In this evaluation, the impact of the EV charging station on the substation capacity is assessed through a high EV penetration scenario. The proposed method is applied in Cuenca, Ecuador. Results show the effectiveness of MCDA in assessing the impact of charging stations on power distribution systems ensuring suitable system operation under substation capacity reserves.

Efficient Operation Algorithm of UAVs for Tourist Safety: Case of the Hallasan Mountain Trail in Jeju Island

Tourist safety is one of the most important factors for tourists when choosing a tourism destination. Jeju Island’s Hallasan Mountain Trail is a trail that connects the mid-slope of Hallasan, the main mountain on Jeju volcanic island, and boasts very beautiful scenery. However, tourist safety has become an issue as accidents continue to occur every year. In this study, an efficient operation algorithm that can minimize the total cost with a mixed-integer linear programming (MILP) model is developed, considering the introduction of a UAV patrol system on the Hallasan Mountain Trail, which is difficult to access by vehicles. The application of different speeds for patrol and nonpatrol routes, the selection of candidate sites for UAV stations with easy vehicle access, and the sensitivity analysis of patrol speed and maximum operation time considering the performance improvement of UAVs are the contributions of this study. The results show that stations are installed as close as possible to the trail courses and that lower-performance UAVs are utilized that can patrol the trail courses at a given time. The sensitivity analysis also confirmed that the total cost can be minimized by reducing the number of stations and UAVs or replacing higher-performance UAVs with lower-performance UAVs.

A novel optimization model for tackling capacity challenges in natural gas gathering systems

In the layout optimization of natural gas gathering pipeline network, a lot of work has been done, but few scholars focus on the capacity optimization of gas gathering station (short for station). The structural relationship parameters of dendritic pipeline network and the flow parameters of pipeline network are complicated, which makes it difficult to establish the model, and at the same time, the existence of nonlinear term causes the difficulty of solving the problem. In the current two stage star-dendritic natural gas gathering pipeline network, pipeline sizes and station capacity are not well taken into account for the optimization of pipeline network layout at the same time. In this paper, a consideration station capacity and pipeline sizes: Two-stage star-dendritic network layout optimization model (SCPSTSDLO-Model) is constructed by integrating the factors of site discrete characteristics, station capacity and pipeline sizes, and considering the hydraulic pressure drop conditions of the network, and a solution framework based on the nonlinear processing is proposed, which can obtain the optimization results of optimal affiliation, station type, station site and pipeline sizes at the same time. Five cases are used to verify the validity and accuracy of the model and algorithm, compare the sensitivity analysis and solution algorithm, compare the hydraulic pressure drop conditions, and analyze the application in different forms of pipeline networks. The results show that the SCPSTSDLO-Model and the solution method established in this paper can obtain the optimal solutions under different topologies according to the application requirements, taking into account the capacity of stations and pipeline sizes.

Uncertainty Quantification in Radial Anisotropy Models Based on Transdimensional Bayesian Inversion of Receiver Functions and Surface-Wave Dispersion: Case Study Sri Lanka

ABSTRACT In geophysical inference problems, quantification of data uncertainties is required to balance the data-fitting ability of the model and its complexity. The transdimensional hierarchical Bayesian approach is a powerful tool to evaluate the level of uncertainty and determine the complexity of the model by treating data errors and model dimensions as unknown. In this article, we take account of the uncertainty through the whole procedure, thus developing a two-step fully Bayesian approach with coupled uncertainty propagation to estimate the crustal isotropic and radial anisotropy (RA) model based on Rayleigh and Love dispersion as well as receiver functions (RFs). First, 2D surface-wave tomography is applied to determine period-wise ambient noise phase velocity maps and their uncertainty for Rayleigh and Love waves. Probabilistic profiles of the isotropic average VS and RA as a function of depth are then derived at station sites by inverting the local surface-wave dispersion and model errors and RFs jointly. The workflow is applied to a temporary seismic broadband array covering all of Sri Lanka. The probabilistic results enable us to effectively quantify the uncertainty of the final RA model and provide robust inferences. The shear-wave velocity results show that the range of Moho depths is between 30 and 40 km, with the thickest crust (38–40 km) beneath the central Highland Complex. Positive RA (VSH>VSV) observed in the upper crust is attributed to subhorizontal alignment of metamorphic foliation and stretched layers resulting from deformation. Negative RA (VSV>VSH) in the midcrust of central Sri Lanka may indicate the existence of melt inclusions and could result from the uplift and folding process. The positive RA in the lower crust could be caused by crustal channel flow in a collision orogeny.

Strategies and models for optimal EV charging station site selection

Abstract In numerous countries worldwide, adopting electric vehicles (EVs) is gaining momentum as a proactive measure to mitigate the detrimental environmental impacts of traditional fuel-powered automobiles. This shift drives exponential growth in the adoption of EVs, prompting the need for comprehensive analysis to optimize charging infrastructure requirements. Developing reliable and sustainable charging infrastructure depends on practical and strategic site selection of EV charging stations. The main challenge is finding a charging solution that maximizes efficiency within limited financial resources. The present review critically assesses methodologies for selecting optimal EV charging station sites, considering technical, environmental, social, and economic factors. Special emphasis is given to social factors such as population density and service accessibility, as well as technical factors like vehicle battery life, charging time, and grid capacity. Environmental impact and feasibility are also vital criteria under evaluation. Through a synthesis of insights from various studies, this review provides a comprehensive overview of the existing models used in EV charging infrastructure site selection. The findings contribute valuable insights for decision-makers, city planners, and other stakeholders in creating sustainable EV charging networks amidst the dynamic landscape of electric mobility.

Integrating Depth Measurements From Gaging Stations With Image Archives for Spectrally Based Remote Sensing of River Bathymetry

AbstractRemote sensing can be an effective tool for mapping river bathymetry, but the need for direct measurements to calibrate image‐derived depth estimates impedes broader application of this approach. One way to circumvent the need for field campaigns dedicated to calibration is to capitalize upon existing data. In this study, we introduce a framework for Bathymetric Mapping using Gage Records and Image Databases (BaMGRID). This workflow involves retrieving depth measurements made during gaging station site visits, downloading archived multispectral images, and then combining these two data sets to establish a relationship between depth and reflectance. We developed a processing chain that involves using application programming interfaces to obtain both depth measurements made during site visits and images centered on the gage and then linking depth to reflectance via an optimal band ratio analysis (OBRA) algorithm modified for small sample sizes. Applying this workflow to selected gages within two river basins indicated that depth retrieval from multispectral satellite images could be highly accurate, but with variable results from one image to the next at a given site. High resolution aerial photography was less conducive to bathymetric mapping in one of the basin considered. Of the four predictors of depth retrieval performance we evaluated (mean and standard deviation of depth, width, and an index of water clarity), only width was consistently significantly correlated with OBRA R2 (p < 0.026). Currently, BaMGRID is best‐suited for site‐by‐site analysis to support practical applications at the reach scale; continuous, basin‐wide mapping of river bathymetry will require additional research.

An earthquake spectra parameters near the new capital administrative city, Egypt

Seven Strong Motion stations recorded an earthquake of Mw 3.8 on December, 31 2018 on the Cairo- Suez southern road. The furthest NUB station is about 302 km from the epicenter, whereas the closest KOT station is 10 km away. This event is considered the first recorded acceleration event near the new capital administrative city. The KOT station has the highest recorded acceleration, measuring 16.38 Gal. The peak ground velocity (PGV), peak ground displacement (PGD), and Pseudo acceleration values for the same station are calculated to be 0.00109 m/s, 0.114 cm, and 0.0731 g, respectively. An intensity map was created for this event as a questionnaire gathered information from Facebook and EMSC. In the vicinity of Qattamiya Observatory, the maximum observed intensity is (IV). According to the geological information and average shear wave velocity calculated from previous studies, We classified the station sites into soil types B, C, and D. We correlated them with the HVSR analysis obtained from S-wave earthquake data. The maximum amplification factor (A0) was found for a BANH station equal to 5.8 and equivalent to the Fundamental frequency (F0) of 3 Hz. The minimum amplification factor was found to be for stations of ISM & NUB with A0 equal to 2.8 and 1.6, respectively, while Fundamental frequency (F0) was 0.7 and 2 Hz, respectively. At the KOT station, the response design spectrum indicates a maximum value of 70 Gal. The new Egyptian capital administrative city is strongly advised to set up an early warning system and structural earth monitoring to manage the risk assessment of any potential seismic activities in the future.

Development of a multi-layer canopy model for E3SM Land Model with support for heterogeneous computing

The vertical structure of vegetation canopies creates micro-climates. However, the land components of most Earth System Models, including the Energy Exascale Earth System Model (E3SM), typically neglect vertical canopy structure by using a single layer big-leaf representation to simulate water, CO2, and energy exchanges between the land and the atmosphere. In this study, we developed a Multi-Layer Canopy Model for the E3SM Land Model to resolve the micro-climate created by vegetation canopies. The model developed in this study re-implements the CLM-ml_v1 to support heterogeneous computing architectures consisting of CPUs and GPUs and includes three additional optimization-based stomatal conductance models. The use of Portable, Extensible Toolkit for Scientific Computation provides a speedup of 25–50 times on a GPU relative to a CPU. The numerical implementation of the model was verified against CLM-ml_v1 for a month-long simulation using data from the Ameriflux US-University of Michigan Biological Station site. Model structural uncertainty was explored by performing control simulations for five stomatal conductance models that exclude and include the control of plant hydrodynamics (PHD) on photosynthesis. The bias in simulated sensible and latent heat fluxes was lower when PHD was accounted for in the model. Additionally, six idealized simulations were performed to study the impact of three environmental variables (i.e. air temperature, atmospheric CO2, and soil moisture) on canopy processes (i.e. net CO2 assimilation, leaf temperature, and leaf water potential). Increasing air temperature reduced net CO2 assimilation and increased air temperature. Net CO2 assimilation increased at higher atmospheric CO2, while decreasing soil moisture resulted in lower leaf water potential.

Bi-Level Planning of Electric Vehicle Charging Stations Considering Spatial–Temporal Distribution Characteristics of Charging Loads in Uncertain Environments

With the increase in the number of distributed energy resources (DERs) and electric vehicles (EVs), it is particularly important to solve the problem of EV charging station siting and capacity determination under the distribution network considering a large proportion of DERs. This paper proposes a bi-level planning model for EV charging stations that takes into account the characteristics of the spatial–temporal distribution of charging loads under an uncertain environment. First, the Origin–Destination (OD) matrix analysis method and the real-time Dijkstra dynamic path search algorithm are introduced and combined with the Larin Hypercube Sampling (LHS) method to establish the EV charging load prediction model considering the spatial and temporal distribution characteristics. Second, the upper objective function with the objective of minimizing the cost of EV charging station planning and user charging behavior is constructed, while the lower objective function with the objective of minimizing the cost of distribution network operation and carbon emission cost considering the uncertainty of wind power and photovoltaics is constructed. The constraints of the lower-layer objective function are transformed into the upper-layer objective function through Karush–Kuhn–Tucker (KKT) conditions, the optimal location and capacity of charging stations are finally determined, and the model of EV charging station siting and capacity determination is established. Finally, the validity of the model was verified by planning the coupled IEEE 33-node distribution network with the traffic road map of a city in southeastern South Dakota, USA.

Energy performance of off-grid green cellular base stations

The most energy-hungry parts of mobile networks are the base station sites, which consume around 60−80% of their total energy. One of the approaches for relieving this energy pressure on the electricity grid infrastructure and reducing the Operational Expenditures (OPEX) is to power base stations with renewable energy. However, the design of a green mobile network requires the dimensioning of the energy harvesting and storage systems through the estimation of the network’s energy demand. Therefore, this paper develops a diffusion-based modelling framework for solar-powered green off-grid base station sites. We apply this framework to evaluate the energy performance of homogeneous and hybrid energy storage systems supplied by harvested solar energy. We present the complete analysis, with numerical examples, to study the relationship between the design parameters and the energy performance metrics. The numerical computations demonstrate how the proposed framework can be applied to evaluate homogeneous and unconventional hybrid energy storage systems.

Examining fire service coverage and potential sites for fire station locations in Kathmandu, Nepal

In the densely populated Kathmandu Metropolitan City, inadequate fire service coverage poses a significant fire risk. With the demand for fire services increasing due to rapid growth in population and infrastructure development, the limited number of fire stations, insufficient human and physical resources, poor street connectivity, narrow streets, and delays in response to calls for emergency services have resulted in significant property loss, environmental impact, serious injuries, and even loss of life. Although a substantial body of research exists on understanding fire patterns and fire station site selection in developed countries, there is a noticeable lack of similar research in developing countries, which can be attributed to a lack of available data and interest. This study utilises ward-level urban fire incident data in Kathmandu from July 2019 to February 2023 to examine the spatial pattern of fire risk, fire service coverage and identify potential sites for fire stations. Results reveal a significantly higher fire rate in the central part of the city. Fire rescue service cannot reach most areas in Kathmandu within 5 min, with some areas exceeding 10 min response time. The findings from this study will enable fire agencies to enhance the operational efficiency of emergency response and help develop policies and programs related to fire risk prevention and mitigation. This includes targeted education and awareness campaigns and the allocation of finite fire service resources, such as the strategic placement of fire stations.

Optimizing shared bike systems for economic gain: Integrating land use and retail

This study explores the optimization of shared-bike station locations, emphasizing the cycling role in low-carbon mobility and local economic revitalization. A unique aspect of this research is the incorporation of the retail activity index in determining station locations, land-use mix, transit proximity, and population and employment densities. A novel weighting method, combining the multi-criteria decision method “Criteria Importance Through Inter-criteria Correlation (CRITIC)” with Ensemble-based predictive algorithms (Random Forest, XGBoost, Gradient Boosting), was developed to evaluate these factors. Using a geographic information system-based spatial model, this study conducted a suitability analysis by applying these weights to assess and propose bike station sites. The findings from the Seoul case indicate a balanced land-use mix, a low number of transactions linked to cyclists, and a significant gap in multimodal transport integration. The weighting results revealed a high priority for transit proximity and retail activity in station placement. Suitability scores across districts revealed significant variability, suggesting the need for greater integration of cycling policies into urban planning. High-scoring districts, such as Gangnam-gu and Seocho-gu, offer insights for improvements in less suitable areas. This study advocates expanded cycling planning, focusing on enhancing access to public transit and retail areas.

Sustainable and smart electric bus charging station deployment via hybrid spherical fuzzy BWM and MULTIMOORA framework

This study aims to assist public bus operators in locating electric bus charging station (EBCS) facilities from a smart and sustainable view. The selection of the most suitable EBCSs from various possible candidates involves a sophisticated decision-making procedure in terms of several contradictory criteria with imprecise information. The novelty of the study resides in exploring the EBCS site selection problem with spherical fuzzy sets (SFSs), which have shown remarkable effectiveness in limiting information loss by seizing ambiguous, and uncertain data. In this regard, a novel best–worst method (BWM) incorporating Multi-objective optimization via full multiplicative form ratio analysis (MULTIMOORA) methodology in the spherical fuzzy context is proposed to choose the optimal locations for EBCSs. The integrated framework combines the adaptability of the spherical fuzzy BWM (SF-BWM) for determining the criteria weights with the convenience of spherical fuzzy MULTIMOORA (SF-MULTIMOORA) approach for ranking the alternatives. A case study for Istanbul is provided to substantiate the propounded technique and to confirm its viability and efficiency. In the course of making a decision, a four-level hierarchical structure consisting of five main and 22 sub-criteria is built and the comparison matrices are reviewed by a panel of seven experts. A sensitivity analysis is executed, and the results demonstrate that the propositioned approach produces outcomes that are quite robust and consistent. Hence, the findings of this research can benefit public bus operators in choosing the ideal sites for electric charging stations. Finally, the formulated generic methodology is also easily applicable to diverse and complex multiple-criteria problems in the spherical fuzzy domain.

A methane monitoring station siting method based on WRF-STILT and genetic algorithm

Reducing methane emissions in the oil and gas industry is a top priority for the current international community in addressing climate change. Methane emissions from the energy sector exhibit strong temporal variability and ground monitoring networks can provide time-continuous measurements of methane concentrations, enabling the rapid detection of sudden methane leaks in the oil and gas industry. Therefore, identifying specific locations within oil fields to establish a cost-effective and reliable methane monitoring ground network is an urgent and significant task. In response to this challenge, this study proposes a technical workflow that, utilizing emission inventories, atmospheric transport models, and intelligent computing techniques, automatically determines the optimal locations for monitoring stations based on the input quantity of monitoring sites. This methodology can automatically and quantitatively assess the observational effectiveness of the monitoring network. The effectiveness of the proposed technical workflow is demonstrated using the Shengli Oilfield, the second-largest oil and gas extraction base in China, as a case study. We found that the Genetic Algorithm can help find the optimum locations effectively. Besides, the overall observation effectiveness grew from 1.7 to 5.6 when the number of site increased from 1 to 9. However, the growth decreased with the increasing site number. Such a technology can assist the oil and gas industry in better monitoring methane emissions resulting from oil and gas extraction.

Behavior, site use and demographics of shortfin devil rays, Mobula kuhlii, at a newly-discovered cleaning area in South Africa

Many shark and ray species show affinity to specific sites, making these areas critical for their survival. These include cleaning stations: locations on reefs where cleaner fish remove parasites and clean wounds, which is important for maintaining health. Cleaning stations also function as social gathering sites, or resting points, where courtship and mating can occur. In this study, we identify an aggregation site for the shortfin devil ray, Mobula kuhlii (Family Mobulidae) within the Aliwal Shoal Marine Protected Area in KwaZulu-Natal (KZN), South Africa, and document their behavior. Remote underwater video was used to collect footage of M. kuhlii being cleaned by blue streak cleaner wrasse, Labroides dimidiatus. Generalized additive models (GAMs) were used to assess environmental predictors of M. kuhlii presence on Aliwal Shoal. Mixed models were used to assess the same environmental predictors and their correlation with mean M. kuhlii cleaning duration and number of L. dimidiatus bites per second at the identified cleaning station site. M. kuhlii were present in 56% of observation days, with group numbers up to > 150 individuals. Sea surface temperature was a significant predictor for M. kuhlii presence, while a north to south current was significantly associated with longer mean cleaning durations. These results support findings of mobulid studies in KZN that show increased habitat use during summer temperatures (24–25 °C) and suggest these sites to be important for individual health and social interaction. We hope these findings can be used for development of location-specific management plans to safeguard this Endangered species.

Shallow crustal imaging beneath NW Indian terrane from teleseismic P-wave coda using a Bayesian approach

Deciphering the crustal architecture of the Cretaceous Barmer-Sanchore Basin (BSB) and Neoproterozoic provinces in the NW Indian Shield presents significant challenges due to extensive sediment deposition and widespread felsic igneous rocks. To address such intricacies, we employ a transdimensional Bayesian joint inversion of teleseismic P-wave polarizations and receiver functions to resolve the detailed structure of the upper 10 km of the crust beneath 29 seismic stations deployed in the study area. The new data is analyzed separately within each of the three different backazimuthal groups to identify any strong variations in the shallow subsurface structure and derive a more robust, averaged model for every station site. The resulting 1-D models reveal heterogeneous sediment cover in BSB and Marwar Basin, with respective thicknesses ranging from ∼1.3–5.5 km and 1–1.5 km. Remarkably low shear-wave velocities (Vs < 1.0 km/s) of sediments with increased Vp/Vs (>2.3) are observed in BSB and adjacent areas, which are related to highly unconsolidated Quaternary-Tertiary deposits. The Marwar Basin is imaged to have 0.3–0.5 km of uncompacted sediments that overlie 0.5–1 km thick Marwar Supergroup sequence. The Erinpura Granites (EG) and Malani Igneous Province (MIP) are characterized by a variably thick sediment blanket, typically ranging within 1 km in thickness. The Vs of the sediments in these regions exceeds 2.4 km/s that possibly corresponds to Mesozoic and older sediments. The basement beneath EG exhibits Vs faster than 3.4 km/s, consistent with moderately metamorphosed granites. In MIP, the basement is characterized by non-uniform shear velocities, suggesting the influence of intrusions linked to magmatic episodes in NW India.

A comprehensive framework for electric vehicle charging station siting along highways using weighted sum method

The proliferation of electric vehicles (EVs) has led to an increased demand for strategically located EV charging stations (EVCSs) to ensure a balanced and accessible charging network. The siting of EVCS involves a multifaceted process that includes technological, economic, social, geographical, and environmental factors. An EVCS network should deliver high-quality service, alleviate drivers' range anxiety, and be compatible with renewable energy system integration, while also accounting for technical infrastructure and future expansion expectations. Resting areas along highways, equipped with amenities and grid connections, emerge as potential sites for EVCS installation. Accordingly, this study presents an EVCS siting framework along highways, integrating expert opinions from diverse disciplines. The proposed method employs expert opinions to weigh site selection criteria and then uses a clustering-based approach to identify suitable locations for EVCS siting, utilizing the weighted sum method. According to the experts, the most important criterion is determined as the service level of the candidate point with a weight of 0.375, followed by traffic density (0.218), and proximity to connection roads (0.215). The method is tested both on a test highway and the Edirne-Ankara highway, which is the most heavily used in Türkiye. The results demonstrate that the test highway identifies 13 out of 18 optimal locations using the clustering strategy. Similarly, the siting method identifies 18 out of 31 optimal EVCS locations along the Edirne-Ankara highway ensuring the distance constraints. This approach is scalable and adaptable for application on highways in various countries where EVCS infrastructure is still developing.

Dynamic assessment of the Malaysia Square Pedestrian Bridge, Battersea Power Station transformation

In this article, Artur Soczawa-Stronczyk discusses his work on a project to assess the dynamic characteristics of the Malaysia Square Pedestrian Bridge – a structure built as part of the transformation of the former Battersea Power Station site in London. The project drew on Artur's MSc and doctoral research into pedestrian-induced vibration to establish that additional damping measures were not required for the bridge. The case study was highly commended in the IStructE's 2022 Research into Practice Case Study competition.

Charging Station Site Selection Optimization for Electric Logistics Vehicles, Taking into Account Time-Window and Load Constraints

In order to improve the efficiency of the “last-mile” distribution in urban logistics and solve the problem of the difficult charging of electric logistics vehicles (ELVs), this paper proposes a charging station location optimization scheme for ELVs that takes into account time-window and load constraints (TW-LCs). Taking the optimal transportation path as the objective function and considering the time-window and vehicle load constraints, a charging station siting model was established. For the TW-LC problem, an improved genetic algorithm combining the farthest-insertion heuristic idea and local search operation was designed. Three different types of standardized arithmetic examples, C type, R type, and RC type, were used to test the proposed algorithm and compare it with the traditional genetic algorithm. The results indicate that, under the same conditions, compared to the traditional genetic algorithm, the improved genetic algorithm reduced the optimal path length by an average of 11.12%. It also decreased the number of charging stations selected, the number of vehicles in use, and the algorithm complexity by 22.97%, 13.71%, and 46.81%. Building on this, a case study was conducted on the TW-LC problem in a specific area of Chongqing, China. It resulted in a 50% reduction in the number of charging stations and a 25% reduction in the number of vehicles selected. In terms of economic indicators, the proposed algorithm improves unit electricity sales by 73.88% and reduces the total annualized cost of the logistics company by 18.81%, providing a theoretical basis for the subsequent promotion of ELVs.

Evaluation of Aeris mid-infrared absorption (MIRA), Picarro CRDS (cavity ring-down spectroscopy) G2307, and dinitrophenylhydrazine (DNPH)-based sampling for long-term formaldehyde monitoring efforts

Abstract. Current formaldehyde (HCHO) measurement networks rely on the TO-11A offline chemical derivatization technique, which can be resource intensive and limited in temporal resolution. In this work, we evaluate the field performance of three new commercial instruments for continuous in situ formaldehyde monitoring: the Picarro cavity ring-down spectroscopy G2307 gas concentration analyzer and Aeris Technologies' mid-infrared absorption Pico and Ultra gas analyzers. All instruments require regular drift correction, which is accomplished through instrument zeroing using dinitrophenylhydrazine (DNPH)-coated cartridges, Drierite, or molecular sieves, while heated Hopcalite failed to remove all incoming HCHO. We show that a modified precision estimate accounting for regular instrument zeroing results in values of 0.09, 0.20, and 0.22 ppb at a 20 min integration time for the G2307, Ultra, and Pico, respectively. After applying standard addition and dynamic dilution calibrations, all instruments agreed within 13 % and were well correlated with each other (all r ≥ 0.90). TO-11A HCHO observations resulted in a normalized mean bias of −58 % compared to co-located Picarro G2307 measurements (r=0.62, slope = 0.38, int = 0.07 ppb HCHO). Using a 6-month deployment period in the Atlanta metropolitan area, we determined that the Picarro G2307 and Aeris units have sufficient accuracy and precision to capture the Atlanta spatial HCHO gradient. We find that midday HCHO concentrations have decreased by 22.3 % since 1999 in the city's urban core, and DNPH measurements at a nearby Photochemical Assessment Monitoring Station (PAMS) site show a greater decrease of 53 %.