Development and Validation of a Sub-National, Satellite-Based Land-Use Regression Model for Annual Nitrogen Dioxide Concentrations in North-Western China.

Existing national- or continental-scale models of nitrogen dioxide (NO2) exposure have a limited capacity to capture subnational spatial variability in sparsely-populated parts of the world where NO2 sources may vary. To test and validate our approach, we developed a land-use regression (LUR) model for NO2 for Ningxia Hui Autonomous Region (NHAR) and surrounding areas, a small rural province in north-western China. Using hourly NO2 measurements from 105 continuous monitoring sites in 2019, a supervised, forward addition, linear regression approach was adopted to develop the model, assessing 270 potential predictor variables, including tropospheric NO2, optically measured by the Aura satellite. The final model was cross-validated (5-fold cross validation), and its historical performance (back to 2014) assessed using 41 independent monitoring sites not used for model development. The final model captured 63% of annual NO2 in NHAR (RMSE: 6 ppb (21% of the mean of all monitoring sites)) and contiguous parts of Inner Mongolia, Gansu, and Shaanxi Provinces. Cross-validation and independent evaluation against historical data yielded adjusted R2 values that were 1% and 10% lower than the model development values, respectively, with comparable RMSE. The findings suggest that a parsimonious, satellite-based LUR model is robust and can be used to capture spatial contrasts in annual NO2 in the relatively sparsely-populated areas in NHAR and neighbouring provinces.

1. Introduction

Air pollution is implicated in an estimated 4 million premature deaths globally each year [1]. The increasing global health burden attributable to air pollution is driven by population and economic growth, reflecting rapid industrialisation and urbanization, particularly in developing nations [2,3]. The highest burden of disease due to outdoor air pollutants as quantified by Disability Adjusted Life Years (DALYs) or years lost due to ill-health has consistently been recorded in Asia and Africa (2029–2751 age-standardised DALYs per 100,000 population) [4,5]. Similarly, global death rates (age-standardised) attributable to ambient air pollution vary by a factor of 10 between high-income countries (8 per 100,000 population) and low-to-middle-income countries (LMICs, 85 per 100,000 population) [1]. These severe health inequalities highlight the need for valid estimates of air pollution exposure in LMICs.

Of the major outdoor air pollutants, nitrogen dioxide (NO2) is one of the key indicators of anthropogenic sources, such as traffic and industrial emissions [6,7]. NO2 is also an important precursor of other important air pollutants, such as ground-level ozone. However, the spatially heterogenous nature and diverse range of NO2 sources presents several challenges for modelling [8,9]. For instance, NO2 levels have been shown to decay rapidly within ~150 to 200 m of major roads [6,9]. Industrial activities such as combustion of fossil fuels in power stations can affect NO2 concentrations more subtly but over greater distances [10]. Generally, in urban settings, NO2 sources include coal combustion and vehicle emissions, whilst in less populated areas, contributors to NO2 levels are more likely to be regional-scale industrial emissions [11,12].

The rural and urban contrast in terms of NO2 sources is particularly apparent in China. At the start of this century, environmental policies in growing urban centres, such as Beijing and Shanghai, have led to a transition from coal to cleaner energy sources. There have also been restrictions on high-emitting vehicles [12,13]. In rural areas, however, coal is still widely used for power generation and at household level for cooking and heating. In farming areas, the use of agricultural fertilisers, machinery, and equipment can also affect NO2 concentrations [14]. As a result, NO2 exposure assessment requires consideration of both local and regional sources of the pollutant. This is particularly relevant to China, which has distinctively different sources of NO2 in large cities compared with sparsely populated rural areas in the north-west of the country [12,14].

Land-use regression (LUR) is an approach to NO2 exposure assessment modelling that has been used since the mid 1990s. More recently, however, LUR models have sought to combine satellite observations and ground level measurements with known predictors of NO2 pollution [15,16]. An LUR model offers improved spatial coverage and discrimination compared with what is achievable with fixed monitoring stations alone, as it can be applied to estimate air pollution exposure even in non-measured locations at an intra-urban or local scale [17,18]. There has been increased research interest in satellite-based LUR modelling of NO2, where tropospheric NO2 and other remotely sensed variables are used as a spatially-varying predictor of measured ground-level concentrations. However, existing literature is limited to global, continental models or large urban centres [7,15,16,18,19,20].

Current LUR models in China have potential limitations regarding their coarse spatial resolution (>10 km) and temporal span, which may limit validity of estimates of fine-scale intra-urban NO2 concentrations [21,22,23]. Additionally, the relatively limited number of monitoring sites (sample size) included in model development in previous sub-national models for China may limit the validity of their NO2 predictions [21,23,24,25,26]. The use of less spatially refined (13 × 24 km2 at nadir) NO2 satellite measurements and other geographically varying predictors is also a limiting feature in current NO2 models in the country [21,23,24,25,26,27]. Whilst spatially refined and cross-validated global and national LUR models exist for China, their validity and generalisability to sparsely populated areas is unknown, as is the question of whether models with smaller spatial extent (e.g., sub-national) are valid in those areas [7,28].

As existing air pollution models have focused on understanding the spatial variability of NO2 in more densely inhabited local urban environments, and at a national scale, the ability of such models to assign exposure associated with rural emissions is unclear. Therefore, the objective of this study was to develop a LUR model for annual NO2 levels in Ningxia Hui Autonomous Region (NHAR) a sparsely populated province with income levels and life expectancy well below the average for China [29]. The development of the model was motivated by intention of examining the effects of NO2 and other ambient pollutants on respiratory disease outcomes in Ningxia Hui Autonomous Region. We aimed to capture important sources of NO2 exposure in a sparsely-populated region of China for which there are no comparable sub-national models. We also sought to independently validate the model against historical measurements. The findings presented in this study may inform future NO2 exposure assessment applications outside the study area by identifying important predictors of NO2 in other rural and semi-rural regions in China and beyond.

2. Materials and Methods

2.1. Study Area

Ningxia Hui Autonomous Region is the smallest provincial-level autonomous region located in Northwest China. Ningxia’s territory covers an area of 66,400 km2. Administratively, NHAR is divided into five prefecture level cities (Yinchuan (2.3 million population); Shizuishan (734,400 population); Wuzhong (1.4 million population); Guyuan (1.2 million population); and Zhongwei (1.2 million population)) that are subsequently subdivided into counties/districts/county-level cities, townships, and villages [29,30,31]. The total population of NHAR exceeds 7.2 million people (>46% of the population inhabit rural areas), ranking 30th and 28th in population size and density (108 persons per km2), respectively, of 34 provincial level administrative divisions in China [31]. NHAR has one of the lowest GDP outputs in the country [29,31].

The study area was extended to a 500 km radius centred on Ningxia province as a balance between increasing the number of monitoring stations without substantially changing the relationship between predictors and NO2 (i.e., the study area was assumed to be broadly similar to NHAR in NO2 sources and spatial variability). By expanding the study area beyond Ningxia, the number of available monitoring stations increased from 19 to 123 (Table S1). As a result, the study area also encompassed parts of Inner Mongolia Autonomous Region (24 million population), Gansu (25 million population), as well as Shaanxi Province (24 million population) (Figure 1). Including Shaanxi Province ensured inclusion of important regional sources and predictors of NO2 pollution, such as mining sites and coal power stations. Dominant sources of NO2 emissions are primarily located in south-eastern parts of Shaanxi, namely in Tongchuan and Xi’an as well as in Baoji. Shaanxi Province accounted for 34% of monitoring sites in the study region. Nitrogen oxide emitting iron ore processing sites located in western areas of Gansu Province (Lanzhou, Linxi, and Baiyin) were also captured in addition to petrochemical processing bases and refining industries found in north-east of Yinchuan in Ningxia Province [32].

Figure 1

Location and distribution of air quality monitoring sites in study area.

2.1.1. Measured NO2

Continuous, hourly ground-level NO2 measurements were obtained for the period of 1 January to 31 December 2019 through OpenAQ [33]. This year was the first year for which hourly NO2 measurements were publicly available for a full calendar year. OpenAQ is an open-source portal that aggregates government-measured ambient air pollution data and obtains air monitoring data for China from the Chinese Environmental Monitoring Center (CEMC) as well as provincial-level Environmental Monitoring Agencies [33]. All measurements were performed using standard chemiluminescence methods, following Chinese Ambient Air Quality Standards (GB 3095—2012) and Ambient Air Quality Index (AQI) technology (HJ 633—2012).

Quality control and consistency checks were applied to the daily monitoring data to exclude monitoring sites with incomplete data (i.e., where measurements were not available for at least 75% of the year) and/or missing coordinates (latitude and longitude to five decimal places). A total of 123 sites were identified within a 500 km radius centred on NHAR. Due to the strong seasonal variation in NO2, we sought to limit the potential for bias due to missing data, and 14 sites were therefore removed (>25% missing daily monitoring data in the year) [34]. Annual averages for 2019 were calculated for the remaining 109 sites.

2.1.2. Variables

Satellite and non-satellite-based predictors of NO2, were extracted from various data sources (Table S2) and are summarised in Table 1. Selection of predictors that could best capture the spatial variability of NO2 was informed by previous global, continental, and national LUR models [7,15,17,19,28]. Predictors were calculated as averages within an area defined by a circle of a specified radius around the monitoring site location (“buffers” with 22 radii ranging from 100 m to 10 km) or point estimates for the monitoring site.

Table 1

Predictor variables included in LUR model development *.

Variable (Units)	Spatial Resolution	Point or Buffer Estimate
OMI (Ozone Monitoring Instrument) NO2 observations (ppb)	10 km	Point
Elevation (m)	90 m	Point
Annual mean Temperature (°C)	1 km	Point
Annual mean Precipitation (mm)	1 km	Point
Distance to nearest major road (km)	-	Point
Distance to nearest coal power station (km)	-	Point
Vegetation cover (%)	250 m	Buffer Average
Tree cover (%)	30 m	Buffer Average
Impervious surfaces (%)	250 m	Buffer Average
Water cover (%)	500 m	Buffer Average
Active Fires (fires/1000 km2/day)	10 km	Buffer Sum
Population density (persons/km2)	1 km	Buffer Average
Major roads (km)	-	Buffer Sum
Minor roads (km)	-	Buffer Sum
Power Plant Emissions (tons of CO2/year)	-	Buffer Sum
Land use by type—Residential, Commercial, and Industrial (%)	-	Buffer Average

* Further infor mation on predictor data sources can be found in Table S2. Buffer estimates were obtained for 22 buffer sizes, ranging from 100 m, 200 m, 300 m, 400 m, 500 m, 600 m, 700 m, 800 m, 1000 m, 1200 m, 1500 m, 1800 m, 2000 m, 2500 m, 3000 m, 3500 m, 4000 m, 5000 m, 6000 m, 7000 m, 8000 m, and 10,000 m.

2.2. Model Development

2.2.1. Variable Selection

A total of 270 predictors were included for initial model development for the year 2019, consisting of 264 buffer and six point variables. Predictors with >75% missing, repeating (i.e., identical), or zero values across the monitoring sites were excluded (n = 88), leaving 182 for model development [16]. The predictors were centred and standardised to aid interpretation and model convergence. The expected coefficient direction of each predictor variable, based on empirical knowledge, was pre-defined (Table S2) following standard LUR modelling practice [35,36]. Briefly, predictors reflecting sources of NO2 such as roads, impervious surfaces, and population density, were defined to be positively associated with NO2 levels. Predictors, such as water and tree and vegetation cover, were defined as being negatively associated with NO2.

2.2.2. Model Development

A supervised forward stepwise linear regression approach was used. Starting with the variable most correlated with measured NO2, variables were added to the model if: (i) the regression coefficient followed the prespecified effect direction, (ii) they did not change the coefficient direction of a variable already in the model, and (iii) they increased adjusted R2 of the model by >1% [16,17,19]. The selection process was repeated until no variables remained that satisfied the inclusion criteria. Forward stepwise linear regression and diagnostic checks was performed using olsrr package in RStudio Version 1.4.1106 [37].

2.2.3. Model Diagnostics and Cross-Validation

Residual plots were visually checked (Figure S1), while collinearity was assessed using variance inflation factor (VIF), with values >3 suggesting collinearity (Table 2). An examination of influential observations was conducted using Cook’s distance (>4/n) and df-beta values (>2/√n) (Figures S2 and S3). For all diagnostic metrics, any suspect sites were sequentially removed and their effect on model composition and performance evaluated. Each of these sites was also manually investigated in Google Earth to understand the nature of the site and inform decisions about retaining or excluding the monitoring site in the final model. For example, one of the sites was situated on a mountain peak, which due to its elevated location, may have been subject to greater dispersion and wind dilution. Concentrations captured by this site (7 ppb) were significantly lower than measurements recorded in other areas in the city of Tianshui (Gansu Province) (26–40 ppb). Moran’s I was used in ArcGIS to detect spatial autocorrelation in model residuals (Figure S4).

Table 2

Summary of final LUR Model.

Final Model Output	Predictor, Buffer (Units)	Β *	SE	Adj. R2	VIF	Contribution to Model (%)
R2: 0.64	Intercept	27.40	0.59
Adj. R2: 0.63	(OMI) tropospheric NO2, (ppb)	6.03	0.83	0.45	1.96	45%
RMSE: 6.1 ppb	Major roads, 5 km (km)	3.02	0.80	0.53	1.76	8%
% RMSE: 21.9 %	Vegetation cover, 1.8 km (%)	−3.43	0.71	0.61	1.47	8%
	Impervious surface, 7 km (%)	1.87	0.76	0.63	1.67	2%

* All predictors, including intercept, were significant at <0.05. Predictors were standardised and mean centred to allow for better interpretation of coefficients (see supplementary information). Predictors are listed in the order they were added to the model. SE, standard error; VIF, variance inflation factor; RMSE, root mean squared error (expressed as absolute in ppb and % of mean NO2 for all sites); ppb, parts per billion.

The prediction error in terms of root-mean-square error (RMSE (in ppb and also as a percentage of the mean of all monitoring sites)), mean absolute error (MAE), and R2 of the final model was evaluated using 5-fold cross-validation. This approach partitions the data into a training and a model building sub-set. The monitoring sites were randomly divided into five sub-sets using the caret package in RStudio Version 1.4.1106 [38]. Four data sub-sets were used to train the model and the remaining group to test the model developed. The cross-validation process was repeated between 50 and 500 times to assess stability of validation metrics (Table S3).

2.2.4. Independent Evaluation

Independent evaluation of the final model was conducted using historical annual NO2 measurements for 2014 and 2015 from monitoring sites in the study area that were not available for developing the 2019 annual model (n = 41) (Table S4). Independent sites were identified using a previously published global scale NO2 model [19]. As with the development sites, the independent monitoring stations also used chemiluminescence to measure NO2 concentrations. The LUR model was applied to estimate the average yearly concentration for these sites (year 2014 = 16; year 2015 = 25) by matching annual time-varying predictor variables to the same period and using the 2019 regression coefficients [16]. The aim was to externally evaluate the LUR model’s performance using new measurements. Specifically, we assessed the validity of applying 2019 model coefficients up to five years earlier. The same validation metrics as cross-validation were used (Table S5).

2.2.5. Model Predictions

Model predictions were gridded at 100 m × 100 m for visualisation of spatial patterns across the study area. NO2 estimates for each grid cell centroid were calculated by multiplying the year 2019 regression coefficients to the corresponding predictor values. Historical predictions (2005–2018) were obtained by matching annual time-varying predictor variables for each year and using the 2019 regression coefficients. Population-weighted annual average NO2 levels for the 358 township-level divisions across NHAR (mean township population 2005–2018 = 17,602 people; mean township area = 144 km2) were calculated by combining the predicted NO2 concentrations with 100-m gridded annual population estimates [39,40].

3. Results

3.1. NO2 Measurements

Mean annual NO2 concentrations at model development sites (n = 105) ranged from 7 to 49 ppb, with an overall mean of 27.6 ppb (SD = 10 ppb) (Figure S5).

3.2. Model Performance

The final LUR model is shown in Table 2. Overall, the model captured 63% of annual NO2 in NHAR (RMSE: 6 ppb (21% of the mean of all monitoring sites)) in 2019. The satellite-derived estimate of tropospheric NO2 accounted for the large majority of prediction performance of the model (incremental adjusted R2 = 45%), while the major roads (8%), vegetation (8%), and impervious surfaces variables (2%) made more modest contributions.

3.3. Model Diagnostics and Cross-Validation

There was no evidence of autocorrelation or other violations of linear regression assumptions in model residuals (Figures S1 and S4). VIF values did not suggest collinearity among the predictors (Table 2). Cook’s distance and df-beta plots identified four sites with pronounced influence on model predictions and coefficients. Initially, each of these monitoring locations were checked for errors in input data and manually searched to assess the nature of the sites and value of retaining them in the data. Additionally, they were sequentially removed and their effect on model parameters and output evaluated (predictors selected, coefficients, significance values of included variables, and adjusted R2). Ultimately, all four were excluded, leaving 105 sites. Minimal changes to model output metrics (RMSE, R2, and MAE) were observed when the cross-validation process was repeated 50 (RMSE: 6.1; R2: 0.63; MAE: 4.9) and 500 times (RMSE: 6.1; R2: 0.64; MAE: 4.9) (Table S3).

3.4. Historical Validation

Results of independent evaluation performed using historical NO2 measurements from monitoring sites not included (n = 41) in model development are shown in Table S5. When the final 2019 LUR model coefficients were applied to the corresponding predictor values for the 16 new sites from year 2014 and additional 25 monitoring locations from 2015, a reduction of −0.09 to −0.10 in R2 (RMSE: 4.9 ppb) was observed. Based on the mean bias (MB) values, our LUR model on average underpredicts historic NO2 concentrations by −0.5 to −0.6 ppb when exposed to new data (Figure S6).

3.5. Model Predictions

Model predictions for annual NO2 concentrations for Ningxia and surrounds are displayed in Figure 2. In less densely populated and more remote areas in the south-western parts of the region, the model estimated NO2 concentrations ranging from 4 to 6 ppb.

Figure 2

Mean annual NO2 model predictions for Ningxia and surroundings (2019), gridded at 100 m (black polygon lines represent provincial level divisions in the region). Inset (A) is of Yinchuan (2.3 million population), capital of Ningxia Province (7.2 million population). Inset (B) highlights local variability in NO2 concentrations in urban locations in Yinchuan (Estimated range: 12–38 ppb) (white polylines in insets (A,B) represent major roads).

Inset A of Figure 2 focuses on Yinchuan (capital of NHAR; population: 2.3 million). Maximum NO2 levels (40 ppb) were observed in congested areas along major roads as well as industrial and commercial districts. This pattern was consistent in other urban areas, such as Xi’an (Shaanxi Province), Lanzhou (Gansu Province), Batou (Inner Mongolia), and Hohhot (Inner Mongolia). Historic predictions (2005–2018) for 358 township-level divisions in Ningxia province are shown in Table 3. Overall, a slightly increasing trend in estimated population-weighted annual average concentrations was observed.

Table 3

Selected percentiles of NO2 concentrations (ppb) predicted for 358 township-level divisions in Ningxia Province (2005–2018).

Year	5th	25th	50th	75th	95th	Unweighted Average	Population Weighted Average
2005	11.0	13.6	14.5	17.4	21.7	14.9	15.6
2006	11.1	14.0	14.7	17.6	21.9	15.4	16.1
2007	11.2	14.1	15.0	17.9	22.0	15.6	16.3
2008	11.4	14.3	15.1	17.9	22.2	15.7	16.4
2009	11.5	14.3	15.1	18.0	22.4	15.9	16.6
2010	11.6	14.3	15.3	18.0	22.6	15.9	16.6
2011	11.8	14.4	15.5	18.0	22.8	16.1	16.7
2012	12.0	14.5	15.6	18.1	23.0	16.5	17.0
2013	12.2	14.7	15.6	18.2	23.1	16.7	17.2
2014	12.3	14.8	15.7	18.4	23.3	16.8	17.3
2015	12.4	14.8	15.8	18.6	23.5	16.7	17.3
2016	12.5	14.8	15.8	18.7	23.7	16.8	17.4
2017	12.7	15.0	15.9	18.7	23.7	16.9	17.5
2018	12.8	15.2	16.1	18.7	23.8	16.9	17.5

4. Discussion

4.1. Overall Findings and Model Performance

The model captured 63% of annual NO2 in NHAR (RMSE: 6 ppb (21% of the mean of all monitoring sites) in 2019 (Table 2). When evaluated for prediction performance and error using independent sites from 2014 and 2015 (n = 41 different sites), a reduction of −0.09 to −0.10 in R2 (RMSE: 4.9 ppb) was observed, which was consistent with other comparative LUR models in the literature (Table S5) [7,20,21,23,26,27,41,42]. As the increases in estimates noted for township-level divisions in NHAR (2005–2018) are minimal (0.2 ppb annual average increase) and within the prediction error of the model (RMSE: 6.5 ppb) and historical validation (2014–2015) (RMSE: 4.9 ppb), there was no clear evidence of an increasing trend.

4.2. Comparison with Existing LUR Models in China

The relatively low adjusted R2 value (63%) of our model highlights the challenges of capturing the diverse range of mobile and static sources of a highly spatially heterogenous pollutant, such as NO2. However, the parsimonious nature of the model (four predictors) may also explain why a modest 9–10% reduction in R2 (RMSE: 4.9 ppb) from training to external validation was observed, suggesting robustness when predicting at locations beyond the training sites, as is done in epidemiological studies. However, the spatial variation in NO2 concentrations explained by the LUR model is within the range of R2 values (0.51–0.78) reported for previous LUR models for NO2 estimation in China [7,20,21,23,26,27,41,42]. Of the eight comparable annual LUR models (five city-level models, R2: 0.51–0.67; two regional models, R2: 0.51–0.67; and one national model, R2: 0.78), only two included satellite observations of NO2 in model development [7,23]. In these models (regional model = R2: 0.61; national model = R2: 0.78), tropospheric NO2 measurements improved model performance by 12 to 13.5 percentage points [7,23]. In our LUR model, which has been developed for a less densely populated region, satellite measurements alone accounted for 45% of prediction of NO2 levels. We assume that the satellite observations captured important regional-scale emissions from other provinces that affected local variations in NO2 in Ningxia.

The reported differences in R2 values among annual LUR models for NO2 in China can be attributed to variation in the availability of data to inform the models. In particular, the distribution and density of monitoring sites, the availability of high-resolution data on land cover, and the use of satellite-based observations of NO2 are important factors that differ among the reported models [7,19]. Furthermore, regional and city-specific models explored smaller buffer radii around each monitoring site (0.3 to 3 km) compared to our model (0.1 to 10 km), which may have affected overall performance [20,21,22,23,26,41]. Improved performance was only noted in one national-level model (78%). Xu and colleagues included ground-level measurement data from China’s expanding national monitoring network consisting of more than 1382 sites [7]. The number and density of sites used likely increased their model’s ability to capture fine-scale distribution patterns of NO2 [7].

Despite methodological differences between our LUR model and existing regional and national models, some similarities were observed. For instance, predictors of traffic-related NO2 pollution (major road length) were included in all models since vehicle emissions are a major source of the pollutant. Similarly, the inclusion of predictors of man-made or impervious surfaces were also prominent in most models, as they account for anthropogenic activities. Likewise, vegetation cover, which is negatively associated with ambient NO2 concentration, was commonly featured in existing LUR models [7,20,21,23,24,25,26,27,28,41,42]. Industrial land use was not chosen by the forward stepwise regression process in our model. Considering the remoteness of the region, there are generally fewer industrial sources of NO2 than in larger metropolitan centres and their surrounding peri-urban areas in eastern China. The study domain also had a lower density of coal power stations (~3 per 10,000 km2) compared to more populated regions in China (~9 per 10,000 km2) [32]. However, tropospheric OMI observations, which explained 45% of the spatial variability of the pollutant in the study area, likely captured industrial contributions to NO2 emissions, in addition to other sources.

4.3. Limitations

There was evidence of underestimation of historical NO2 concentrations by 0.5 to 0.6 ppb (~22%). However, underestimation of NO2 levels is more likely at higher values rather than across the range of NO2 predictions (Figure S7). LUR models are also known to be more reliable in estimating mean exposures than extreme values of NO2 (Xu et al. 2019).

When evaluated using independent sites from 2014 and 2015, which is a more rigorous and realistic test of a model’s performance (including historical performance up to five years prior), a drop of −0.09 to −0.10 in R2 was observed (Table S5). The RMSE of 4.9 ppb obtained for historic validation (2014–2015) and for the 2019 model (RMSE: 6.1 ppb) suggest that the level of uncertainty or error in predictions for 2005–2019 is likely in the range of 4.9 to 6.5 ppb. This decrement in performance and in general RMSE error range is consistent with other LUR models [16]. Possible explanations include the fact that major road length and impervious surface cover were not available historically. However, as tropospheric satellite observations of NO2 (data dating back to 2004) accounted for most of the prediction performance, it provided confidence in applying our model to earlier years.

Additional sources of error may include the use of open-source ground-level monitoring data, which may have been incomplete. As the data were not obtained directly from environmental monitoring agencies in China, sites measuring important local sources of NO2 may have been missed. The locations of air quality monitors in the study area (primarily near government institutions, including schools, universities, and recreational areas) may have limited their ability to capture important spatial gradients relating to industry and airports. Model performance may also be overly optimistic, as we were unable to test our LUR model against more historic ground-level monitoring measurements (apart from 2014 and 2015) due to unavailability of data for the region.

5. Conclusions

The finding that 45% of NO2 was captured by satellite-derived estimates of tropospheric NO2 alone, which date back to 2004, along with the historical validation we undertook, provides support for applying 2019 coefficients to earlier years. The traditional LUR approach we used, which emphasised empirical plausibility and interpretability of coefficients, offered modestly improved predictions (2–4% improvement in R2) compared with other sub-national models for China. More importantly, the model yields valid estimates of annual NO2 in Ningxia and its immediate surrounding areas, and it will be used to estimate NO2 exposure within that spatial domain in an epidemiological study.