Developing a geostatistical simulation method to inform the quantity and placement of new monitors for a follow-up air sampling campaign

Evaluating sample sizes and design for monitoring and characterizing the spatial variations of potentially toxic elements in the soil

Cost-effective, representative and spatial coverage sampling designs are required to monitor the effects of potentially toxic elements (PTEs) in the soil. This study aims to evaluate the minimum sample sizes and placement of soil sampling designs to monitor and characterize the spatial variation of the PTEs (Cu, Zn, Cd, Cr, Pb, and Ni) in the soils. However, there is no standardized approach for evaluating the optimum soil sample size and monitoring location because of the spatial heterogeneity of PTEs in the soil. As a result, three broad techniques were applied. The first step was to use Global Moran's I and q-statistic values to describe the variability of soil PTEs and select appropriate evaluation methods. Second, using simple random sampling (SRS), ordinary kriging (OK), and Mean of Surface with Non-homogeneity (MSN), we estimated and evaluated soil PTEs in the current soil sampling schemes. Finally, MSN and spatial simulated annealing (SSA) optimization techniques were used to assess the required sample sizes and placements in the existing designs. Method performance was evaluated using a standard error (SE) and a relative standard error of the mean (RSE). Except for Zn and Cd, all PTEs tested showed heterogeneous distributions over the area. The MSN lowered the predicted SE by 79-86 % compared with SRS. The OK approach also outperformed the SRS method regarding mean estimated values of soil PTEs by 42-57 %. After SSA refined the initial design, the predicted SE by MSN of Cr and Zn was lowered by 13 % and 39 %, respectively. The MSN was effective with small sample sizes, reducing sample sizes and surveying costs by 39 % after SSA optimized the existing sample numbers. Thus, integrating various sampling strategies may be efficient for building optimal sample designs to monitor PTEs in the soil.

Optimal spatial sampling design for monitoring potentially toxic elements pollution on urban green space soil: A spatial simulated annealing and k-means integrated approach

Sampling design in soil science is critical because the lack of reliable methods and collecting samples requires tremendous work and resources. The aims were to obtain an optimal sampling design for assessing potentially toxic elements pollution using pilot Pb soil samples from the urban green space area of Shanghai, China. Two general steps have been used. The first step is to determine the optimum sample size against improving the prediction accuracy and monitoring costs using the spatial simulated annealing (SSA) algorithm. Secondly, we evaluated their likely placement of new extra sampling points by integrated SSA with k-means (SSA+ k-means) and expert-based (SSA+ expert-based) sampling methods. The improvement of sampling design by the integrated sampling approaches was evaluated using mean kriging variance (MKV), root mean square error (RMSE), and mean absolute percentage error (MAPE). The findings indicated that adding and placing 350 new monitoring points upon the existing sampling design by SSA increased the prediction accuracy by 64.35%. The MKV for the optimized SSA+ k-means sample was lower than by 4.12 mg/kg, 9.46 mg/kg compared with locations optimized by SSA and SSA+ expert-based method, respectively. Optimizing new sampling locations by SSA+ k-means sampling method was reduced MAPE by 9.26% and RMSE by 7.13 mg/kg compared to optimizing by SSA alone. However, there was no improvement in placing the new sampling points in SSA+ expert-based sampling method; instead, it increased the error by 8.11%. This paper shows integrating optimization approaches to evaluate the existing sampling design and optimize a new optimal sampling design.

The Optimization Strategy of the Existing Urban Green Space Soil Monitoring System in Shanghai, China

High concentrations of potentially toxic elements (PTE) create global environmental stress due to the crucial threat of their impacts on the environment and human health. Therefore, determining the concentration levels of PTE and improving their prediction accuracy by sampling optimization strategy is necessary for making sustainable environmental decisions. The concentrations of five PTEs (Pb, Cd, Cr, Cu, and Zn) were compared with reference values for Shanghai and China. The prediction of PTE in soil was undertaken using a geostatistical and spatial simulated annealing algorithm. Compared to Shanghai’s background values, the five PTE mean concentrations are much higher, except for Cd and Cr. However, all measured values exceeded the reference values for China. Pb, Cu, and Zn levels were 1.45, 1.20, and 1.56 times the background value of Shanghai, respectively, and 1.57, 1.66, 1.91 times the background values in China, respectively. The optimization approach resulted in an increased prediction accuracy (22.4% higher) for non-sampled locations compared to the initial sampling design. The higher concentration of PTE compared to background values indicates a soil pollution issue in the study area. The optimization approach allows a soil pollution map to be generated without deleting or adding additional monitoring points. This approach is also crucial for filling the sampling strategy gap.

SPATIAL DISTRIBUTED LAG DATA FUSION FOR ESTIMATING AMBIENT AIR POLLUTION

We introduce spatial (DLfuse) and spatiotemporal (DLfuseST) distributed lag data fusion methods for predicting point-level ambient air pollution concentrations, using, as input, gridded average pollution estimates from a deterministic numerical air quality model. The methods incorporate predictive information from grid cells surrounding the prediction location of interest and are shown to collapse to existing downscaling approaches when this information adds no benefit. The spatial lagged parameters are allowed to vary spatially/spatiotemporally to accommodate the setting where surrounding geographic information is useful in one area/time but not in another. We apply the new methods to predict ambient concentrations of eight-hour maximum ozone and 24-hour average PM2.5 at unobserved spatial locations and times, and compare the predictions with those from several state-of-the-art data fusion approaches. Results show that DLfuse and DLfuseST often provide improved model fit and predictive accuracy when the lagged information is shown to be beneficial. Code to apply the methods is available in the R package DLfuse.

Spatio-Temporal Representativeness of Air Quality Monitoring Stations in Mexico City: Implications for Public Health

Assessment of the air quality in metropolitan areas is a major challenge in environmental sciences. Issues related include the distribution of monitoring stations, their spatial range, or missing information. In Mexico City, stations have been located spanning the entire Metropolitan zone for pollutants, such as CO, NO2, O3, SO2, PM2.5, PM10, NO, NO x , and PM CO . A fundamental question is whether the number and location of such stations are adequate to optimally cover the city. By analyzing spatio-temporal correlations for pollutant measurements, we evaluated the distribution and performance of monitoring stations in Mexico City from 2009 to 2018. Based on our analysis, air quality evaluation of those contaminants is adequate to cover the 16 boroughs of Mexico City, with the exception of SO2, since its spatial range is shorter than the one needed to cover the whole surface of the city. We observed that NO and NO x concentrations must be taken into account since their long-range dispersion may have relevant consequences for public health. With this approach, we may be able to propose policy based on systematic criteria to locate new monitoring stations.

A land use regression model of nitrogen dioxide and fine particulate matter in a complex urban core in Lanzhou, China

BACKGROUND

Land use regression (LUR) models have been widely used to estimate air pollution exposures at high spatial resolution. However, few LUR models were developed for rapidly developing urban cores, which have substantially higher densities of population and built-up areas than the surrounding areas within a city's administrative boundary. Further, few studies incorporated vertical variations of air pollution in exposure assessment, which might be important to estimate exposures for people living in high-rise buildings.

OBJECTIVE

A LUR model was developed for the urban core of Lanzhou, China, along with a model of vertical concentration gradients in high-rise buildings.

METHODS

In each of four seasons in 2016-2017, NO₂ was measured using Ogawa badges for 2 weeks at 75 ground-level sites. PM_2.5 was measured using DataRAM for shorter time intervals at a subset (N = 38) of the 75 sites. Vertical profile measurements were conducted on 9 stories at 2 high-rise buildings (N = 18), with one building facing traffic and another facing away from traffic. The average seasonal concentrations of NO₂ and PM_2.5 at ground level were regressed against spatial predictors, including elevation, population, road network, land cover, and land use. The vertical variations were investigated and linked to ground-level predictions with exponential models.

RESULTS

We developed robust LUR models at the ground level for estimated annual averages of NO₂ (R²: 0.71, adjusted R²: 0.67, and Leave-One-Out Cross Validation (LOOCV) R²: 0.64) and PM_2.5 (R²: 0.77, adjusted R²: of 0.73, and LOOCV R²: 0.67) in the urban core of Lanzhou, China. The LUR models for the estimated seasonal averages of NO₂ showed similar patterns. Vertical variation of NO₂ and PM_2.5 differed by windows orientation with respect to traffic, by season or by time of a day. Vertical variation functions incorporated the ground-level LUR predictions, in a form that could allow for exposure assessment in future epidemiological investigations.

CONCLUSIONS

Ground-level NO₂ and PM_2.5 showed substantial spatial variations, explained by traffic and land use patterns. Further, vertical variation of air pollution levels is significant under certain conditions, suggesting that exposure misclassification could occur with traditional LUR that ignores vertical variation. More studies are needed to fully characterize three-dimensional concentration patterns to accurately estimate air pollution exposures for residents in high-rise buildings, but our LUR models reinforce that concentration heterogeneity is not captured by the limited government monitors in the Lanzhou urban area.