Effects of urban functional fragmentation on nitrogen dioxide (NO2) variation with anthropogenic-emission restriction in China

Abstract

Urban functional fragmentation plays an important role in assessing Nitrogen Dioxide (NO₂) emissions and variations. While the mediated impact of anthropogenic-emission restriction has not been comprehensively discussed, the lockdown response to the novel coronavirus disease 2019 (COVID-19) provides an unprecedented opportunity to meet this goal. This study proposes a new idea to explore the effects of urban functional fragmentation on NO₂ variation with anthropogenic-emission restriction in China. First, NO₂ variations are quantified by an Autoregressive Integrated Moving Average with external variables-Dynamic Time Warping (SARIMAX-DTW)-based model. Then, urban functional fragmentation indices including industrial/public Edge Density (ED) and Landscape Shape Index (LSI), urban functional Aggregation Index (AI) and Number of Patches (NP) are developed. Finally, the mediated impacts of anthropogenic-emission restriction are assessed by evaluating the fragmentation-NO₂ variation association before and during the lockdown during COVID-19. The findings reveal negative effects of industrial ED, public LSI, urban functional AI and NP and positive effects of public ED and industrial LSI on NO₂ variation based on the restricted anthropogenic emissions. By comparing the association analysis before and during lockdown, the mediated impact of anthropogenic-emission restriction is revealed to partially increase the effect of industrial ED, industrial LSI, public LSI, urban functional AI and NP and decrease the effect of public ED on NO₂ variation. This study provides scientific findings for redesigning the urban environment in related to the urban functional configuration to mitigating the air pollution, ultimately developing sustainable societies.

Figure 1

The overview framework. Three components are involved, SARIMAX-DTW-based model for NO₂ variation estimation, human-activity-driven urban functional fragmentation quantification and UFB, UFD, UFCB and UFCD Models for association analysis.

Figure 2

Mean daily observed-predicted NO₂ errors based on training data from three time periods, including Jan. 1st, 2015 to Nov. 1st, 2019, from Jan. 1st, 2015 to Dec. 1st, 2019 and from Jan. 1st, 2015 to Jan. 1st, 2020, to determine the time period of training data for SARIMAX modelling.

Figure 3

Predicted NO₂ concentrations of selected air stations using SARIMAX during Jan. 1st, 2020 to May 1st, 2020. Two air stations #1 and #2 were selected. Red lines indicate the observed NO₂ concentrations, blue lines indicate the predicted NO₂ concentrations, and green lines represent the cut-off date of NO₂ estimation. The blue and grey areas indicate the one and two standard deviation(s) of NO₂ predictions. The map is performed using ArcGIS Pro software (version 2.7, https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview).

Figure 4

Time-series NO₂ variations between observed and predicted NO₂ of 145 air stations before and during lockdown calculated using DTW. (a) NO₂ variations before lockdown; (b) NO₂ variations during lockdown. High values of NO₂ variations represent greater changes among observed and predicted NO₂. The maps are performed using ArcGIS Pro software (version 2.7, https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview).

Figure 5

Urban functional fragmentation of each air station. (a) Industrial ED; (b) Public ED; (c) Industrial LSI; (d) Public LSI; (e) Urban functional AI; (f) Urban functional NP. The maps are performed using ArcGIS Pro software (version 2.7, https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview).

Figure 6

Coefficient changes and 95% CI of urban functional fragmentation characteristics on NO₂ variations based on four models. (a) Industrial ED; (b) Public ED; (c) Industrial LSI; (d) Public LSI; (e) Urban functional AI; (f) Urban functional NP. One unit of industrial ED is associated with − 0.0046 (95% CI − 0.0109 to 0.0017), − 0.0090 (95% CI − 0.0158 to − 0.0023), − 0.0105 (95% CI − 0.0157 to − 0.0053) and − 0.0168 (95% CI − 0.0213 to − 0.0122) NO₂ variations of UFB, UFD, UFCB and UFCD models, respectively. Per unit of public ED is linked with 0.0048 (95% CI − 0.0052 to 0.0148), 0.0024 (95% CI − 0.0083 to 0.0131), 0.0249 (95% CI 0.0166 to 0.0332) and 0.0232 (95% CI 0.0162 to 0.0303) NO₂ variations for four models. NO₂ variations of 0.0103 (95% CI − 0.0218 to 0.0424), − 0.0024 (95% CI − 0.0370 to 0.0321), 0.0360 (95% CI 0.0105 to 0.0616) and 0.0680 (95% CI 0.0454 to 0.0907) of four models are influenced by per unit of industrial LSI. For the public LSI characteristic, one unit of this is associated with 0.0405 (95% CI 0.0026 to 0.0784), − 0.0035 (95% CI − 0.0443 to 0.0373), − 0.0594 (95% CI − 0.0902 to − 0.0287) and − 0.0825 (95% CI − 0.1092 to − 0.0558) NO₂ variations of four models. Per unit of urban functional AI is associated with − 0.1047 (95% CI − 0.2110 to 0.0016), − 0.3165 (95% CI − 0.4308 to − 0.2022), − 0.1253 (95% CI − 0.2167 to − 0.0338) and − 0.1712 (95% CI − 0.2524 to − 0.0899) NO₂ variations of four models. One unit of urban functional NP is associated with − 0.0009 (95% CI − 0.0012 to − 0.0006), − 0.0011 (95% CI − 0.0014 to − 0.0008), − 0.0004 (95% CI − 0.0006 to − 0.0002) and − 0.0008 (95% CI − 0.0010 to − 0.0006) NO₂ variation of four models.

Figure 7

Distribution of urban functions within buffers of selected air stations. 27 sites of 145 air stations with 3 km buffers were displayed. Urban functions including residential, commercial, industrial, transportation and public functions are involved to depict fragmentation characteristics. The maps are performed using ArcGIS Pro software (version 2.7, https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview).