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Building upon paradigms of language and languaging practices as "local" phenomena (Canagarajah, 2013; Pennycook, 2010, Pietikäinen & Kelly-Holmes, 2013), this paper narrates a teacher's experience in an undergraduate seminar in applied language studies as an exploration in transdisciplinarity-as-localization. Taught by the author in 2012-2013, the seminar was intended as an introduction to the politics of societal multilingualism as visible in the linguistic landscape of public texts. As such, it relied upon its own geographic and institutional locality, as well as the diverse conceptual moorings and methodologies of linguistic landscape research (e.g., Blommaert, 2013; Shohamy & Gorter, 2009; Trumper-Hecht, 2010) in order to lead students in interpreting the significance of East Asian languages in the San Francisco Bay Area. However, as the paper endeavors to show, the course's own curriculum--and with it, the locus of teacherly authority--was forced to de-localize as the implementation of curricular ideals in practice revealed heterogeneous and expansive orders of meaning.

Policy interventions and technological advances are mitigating emissions of air pollutants from motor vehicles. As a result, vehicle fleets are expected to progressively combust fuel more efficiently, with a declining ratio of carbon monoxide to carbon dioxide (CO/CO2) in their emissions. We assess trends in traffic combustion efficiency in Los Angeles (LA) and Salt Lake City (SLC) by measuring changes in summertime on-road CO/CO2 between 2013 and 2021 using mobile observations. Our data show a reduction in CO/CO2 in LA, indicating an improvement in combustion efficiency that likely resulted from stringent regulation of CO emissions. In contrast, we observed an increase in CO/CO2 values in SLC. While slower progress in SLC compared to LA may be partially due to a later adoption of vehicle emission regulations in Utah compared to California, differing driving conditions and fleet composition may also be playing a role. This is evidenced by increased CO/CO2 in LA during the COVID-19 pandemic, which led to faster driving speeds and changes to the fleet composition. Our results demonstrate the success of Californias CO-reducing policy interventions and illustrate the impacts of traffic characteristics on vehicle combustion efficiency and air pollutant emissions.

In the past decade, there has been a notable rise in foodborne outbreaks, prominently featuring Escherichia coli as a primary pathogen. This bacterium, known for its prevalence in foodborne illnesses and as a reservoir of antimicrobial resistance, was isolated from raw vegetables, soil, and water samples collected from rooftop and surface gardens in urban (Dhaka North City Corporation; DNCC and Dhaka South City Corporation; DSCC) and peri-urban (Gazipur City Corporation; GCC) areas of Bangladesh. In this study, 145 samples including vegetables (n = 88), water (n = 27) and soils (n = 30) from DNCC (n = 85), DSCC (n = 30), and GCC (n = 30) were analyzed to assess the prevalence of E. coli using culture, biochemical tests, and PCR targeting the malB gene. E. coli was detected in 85 samples, indicating an overall prevalence of 58.62% (95% CI: 50.48-66.31). In urban areas (DNCC and DSCC), the prevalence rates were 44.70% and 80.0%, respectively, with surface gardens showing higher contamination rates (70.83%) than rooftop gardens (46.57%). In the peri-urban GCC, overall prevalence of E. coli was 76.7%, with rooftop gardens more contaminated (93.33%) than surface gardens (60.0%). Antibiogram profiling of 54 randomly selected isolates revealed 100% resistance to ampicillin, with varying resistance to ciprofloxacin (25.92%), tetracycline (14.81%), cotrimoxazole (14.81%), imipenem (9.25%), and fosfomycin (1.0%). Notably, all isolates were susceptible to ceftazidime, gentamicin, chloramphenicol, nitrofurantoin, and cefotaxime. Multidrug resistance (MDR) was found in 14.81% of isolates. The blaTEM gene was present in 81.48% of the isolates, while the tetA gene was detected in 3.70%. These findings underscore the urgent global health concern posed by the significant presence of E. coli in fresh vegetables, highlighting the need for improved safety measures and monitoring to prevent the spread of antimicrobial resistance through the food chain.

BACKGROUND: National-scale linear regression-based modeling may mischaracterize localized patterns, including hyperlocal peaks and neighborhood- to regional-scale gradients. For studies focused on within-city differences, this mischaracterization poses a risk of exposure misclassification, affecting epidemiological and environmental justice conclusions. OBJECTIVE: Characterize the difference between intraurban pollution patterns predicted by national-scale land use regression modeling and observation-based estimates within a localized domain and examine the relationship between that difference and urban infrastructure and demographics. METHODS: We compare highly resolved (0.01 km2) observations of NO2 mixing ratio and ultrafine particle (UFP) count obtained via mobile monitoring with national model predictions in thirteen neighborhoods in the San Francisco Bay Area. Grid cell-level divergence between modeled and observed concentrations is termed localized difference. We use a flexible machine learning modeling technique, Bayesian Additive Regression Trees, to investigate potentially nonlinear relationships between discrepancy between localized difference and known local emission sources as well as census block group racial/ethnic composition. RESULTS: We find that observed local pollution extremes are not represented by land use regression predictions and that observed UFP count significantly exceeds regression predictions. Machine learning models show significant nonlinear relationships among localized differences between predictions and observations and the density of several types of pollution-related infrastructure (roadways, commercial and industrial operations). In addition, localized difference was greater in areas with higher population density and a lower share of white non-Hispanic residents, indicating that exposure misclassification by national models differs among subpopulations. IMPACT: Comparing national-scale pollution predictions with hyperlocal observations in the San Francisco Bay Area, we find greater discrepancies near major roadways and food service locations and systematic underestimation of concentrations in neighborhoods with a lower share of non-Hispanic white residents. These findings carry implications for using national-scale models in intraurban epidemiological and environmental justice applications and establish the potential utility of supplementing large-scale estimates with publicly available urban infrastructure and pollution source information.

Political responses to the COVID-19 pandemic led to changes in city soundscapes around the globe. From March to October 2020, a consortium of 261 contributors from 35 countries brought together by the Silent Cities project built a unique soundscape recordings collection to report on local acoustic changes in urban areas. We present this collection here, along with metadata including observational descriptions of the local areas from the contributors, open-source environmental data, open-source confinement levels and calculation of acoustic descriptors. We performed a technical validation of the dataset using statistical models run on a subset of manually annotated soundscapes. Results confirmed the large-scale usability of ecoacoustic indices and automatic sound event recognition in the Silent Cities soundscape collection. We expect this dataset to be useful for research in the multidisciplinary field of environmental sciences.