Spatial predictors of heavy metal concentrations in epiphytic moss samples in Seattle, WA

Kondo, Michelle C.; Zuidema, Christopher; Morán, Héctor; Jovan, Sarah; Derrien, Monika M.; Brinkley, Weston; Roos, Anneclaire J. De; Tabb, Loni Philip

doi:10.1016/j.scitotenv.2022.153801

Cited by 3 publications

(2 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zechmeister et al (2005) [ 46 ] found that friction between heavy-duty vehicle tires and the road is particularly conducive to the accumulation of pollutants such as As in roadside soil and moss. Many studies have observed arsenic pollution in the soil on both sides of the road [ 47 , 48 , 49 , 50 ]. In this study, we found that As was positively related to the road density, which is consistent with the research of Seker et al (2022) (Seker, et al [ 51 ]) and Qiao et al (2022) (Qiao, et al [ 52 ]).…”

Section: Discussionmentioning

confidence: 99%

Identifying the Local Influencing Factors of Arsenic Concentration in Suburban Soil: A Multiscale Geographically Weighted Regression Approach

Zhu,

Liu,

Jin

et al. 2024

Toxics

View full text Add to dashboard Cite

Exploring the local influencing factors and sources of soil arsenic (As) is crucial for reducing As pollution, protecting soil ecology, and ensuring human health. Based on geographically weighted regression (GWR), multiscale GWR (MGWR) considers the different influence ranges of explanatory variables and thus adopts an adaptative bandwidth. It is an effective model in many fields but has not been used in exploring local influencing factors and sources of As. Therefore, using 200 samples collected from the northeastern black soil zone of China, this study examined the effectiveness of MGWR, revealed the spatial non-stationary relationship between As and environmental variables, and determined the local impact factors and pollution sources of As. The results showed that 49% of the samples had arsenic content exceeding the background value, and these samples were mainly distributed in the central and southern parts of the region. MGWR outperformed GWR with the adaptative bandwidth, with a lower Moran’s I of residuals and a higher R2 (0.559). The MGWR model revealed the spatially heterogeneous relationship between As and explanatory variables. Specifically, the road density and total nitrogen, clay, and silt contents were the primary or secondary influencing factors at most points. The distance from an industrial enterprise was the secondary influencing factor at only a few points. The main pollution sources of As were thus inferred as traffic and fertilizer, and industrial emissions were also included in the southern region. These findings highlight the importance of considering adaptative bandwidths for independent variables and demonstrate the effectiveness of MGWR in exploring local sources of soil pollutants.

show abstract

Section: Discussionmentioning

confidence: 99%

Identifying the Local Influencing Factors of Arsenic Concentration in Suburban Soil: A Multiscale Geographically Weighted Regression Approach

Zhu,

Liu,

Jin

et al. 2024

Toxics

View full text Add to dashboard Cite

show abstract

“…Efforts to identify specific sources affecting residential air quality lie beyond the scope of this study and will be complicated by the density of nearby emission sources and potential for PM to continue mixing and affecting the air long after its initial emission (Johnston & Cushing, 2020). How proximity to the central industrial area and other environmental factors correlate with metals levels in the neighborhoods' residential zones is the focus of a follow-up study already underway (Kondo et al, 2022). These results, along with findings from community actions focused on discerning the potential health risk of the metals, as described in the following section, will support decision making on how to prioritize and approach unresolved questions about pollution sources.…”

Section: Spatial Distributionsmentioning

confidence: 99%

Heavy metals in moss guide environmental justice investigation: A case study using community science in Seattle, WA, USA

et al. 2022

Self Cite

View full text Add to dashboard Cite

Heavy metal concentrations often vary at small spatial scales not captured by air monitoring networks, with implications for environmental justice in industrial‐adjacent communities. Pollutants measured in moss tissues are commonly used as a screening tool to guide use of more expensive resources, like air monitors. Such studies, however, rarely address environmental justice issues or involve the residents and other decision makers expected to utilize results. Here, we piloted a community science approach, engaging over 55 people from nine institutions, to map heavy metals using moss in two industrial‐adjacent neighborhoods. This area, long known for disproportionately poor air quality, health outcomes, and racial inequities, has only one monitor for heavy metals. Thus, an initial understanding of spatial patterns is critical for gauging whether, where, and how to invest further resources toward investigating heavy metals. Local youth‐led sampling of the moss Orthotrichum lyellii from trees across a 250 × 250 m sampling grid (n = 79) and generated data comparable to expert‐collected samples (n = 19). We mapped 21 chemical elements measured in moss, including 6 toxic “priority” metals: arsenic, cadmium, chromium, cobalt, lead, and nickel. Compared to other urban O. lyellii studies, local moss had substantially higher priority metals, especially arsenic and chromium, encouraging community members to investigate further. Potential hotspots of priority metals varied somewhat but tended to peak near the central industrial core where many possible emission sources, including legacy contamination and converge. Informed by these findings, community members successfully advocated regulators for a second study phase—a community‐directed air monitoring campaign to evaluate residents' exposure to heavy metals—as is needed to connect moss results back to the partnership's core goal of understanding drivers of health disparities. This follow‐up campaign will measure metals in the PM10 fraction owing to clues in the current study that airborne soil and dust may be locally important carriers of priority metals. Future work will address how our approach combining bioindicators and community science ultimately affects success addressing longstanding environmental justice concerns. For now, we illustrate the potential to co‐create new knowledge, to help catalyze and strategize next steps, in a complex air quality investigation.

show abstract

Assessment of the air quality in an industrial zone using active moss biomonitoring

Świsłowski,

Rajfur

2023

Int. J. Environ. Sci. Technol.

View full text Add to dashboard Cite

The study aimed to evaluate the level of air contamination in the area around the Kosogorsky industrial enterprise in Tula, Russia, in the winter of 2021/2022. For the study, Pleurozium schreberi, Sphagnum fallax, and Dicranum polysetum mosses were used for the first time in the 3-month active biomonitoring (moss-bag technique). Heavy metals elements (Mn, Fe, Ni, Cu, Zn, Cd, and Pb) were determined using atomic absorption spectrometry. In addition, mercury concentrations were determined with an AMA254 apparatus. The study's results for Fe, Cu, Zn, Pb, and Hg confirm the statistical significance of the species' effect on the accumulation of these elements. Values of relative accumulation factor (RAF) > 1.00 at selected measurement points indicate that the site is contaminated as a result of industrial activities (mainly Mn and Fe). Wind direction influenced moss contamination at selected measurement points, which was confirmed by cluster analysis. In the future, long-term or different seasons biomonitoring studies should be conducted in this area.

show abstract

Spatial predictors of heavy metal concentrations in epiphytic moss samples in Seattle, WA

Cited by 3 publications

References 30 publications

Identifying the Local Influencing Factors of Arsenic Concentration in Suburban Soil: A Multiscale Geographically Weighted Regression Approach

Identifying the Local Influencing Factors of Arsenic Concentration in Suburban Soil: A Multiscale Geographically Weighted Regression Approach

Heavy metals in moss guide environmental justice investigation: A case study using community science in Seattle, WA, USA

Assessment of the air quality in an industrial zone using active moss biomonitoring

Contact Info

Product

Resources

About