Air Quality and the Spatial-Temporal Differentiation of Mechanisms Underlying Chinese Urban Human Settlements
Abstract
:1. Introduction
2. Data Sources and Research Methods
2.1. Data Sources
2.2. Research Methods
2.2.1. Weight Assignment
2.2.2. Spatial-Temporal Differentiation Measurement Model
2.2.3. Calculation Method of Influencing Factors
3. Results
3.1. Spatial-Temporal Distribution Characteristics of Human Settlements
3.1.1. Weight Calculation
3.1.2. Spatial State Mode
3.1.3. Pattern Evolution
3.2. Factors Affecting the Distribution Difference of Human Settlements
4. Discussion
4.1. Analysis of Spatial-Temporal Variation and Influence Factors
4.2. Limitations
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Data Type | Specific Variables | Data Sources |
---|---|---|
Administrative map | Urban planning | Standard map service system (http://bzdt.ch.mnr.gov.cn/) |
Air quality monitoring data | AQI, SO2, NO2, PM2.5, PM10, CO, O3 | 1. Air quality online monitoring and analysis platform (https://www.aqistudy.cn/); 2. Air quality history data (https://aqicn.org/city/dalian/cn/) 3. Atmospheric Composition Analysis Group: Surface PM2.5 (http://fizz.phys.dal.ca/~atmos/martin/?page_id=140) 4. Earth Observing System Data and Information System (https://sedac.ciesin.columbia.edu/data/set/sdei-global-annual-gwr-pm2-5-modis-misr-seawifs-aod/data-download) |
Meteorological data | Temperature, rainfall, wind speed, and humidity | National Meteorological Science Data Center (https://data.cma.cn/data/cdcindex/cid/0b9164954813c573.html) |
Urban social and economic development index data | Evaluation index: highway freight volume, green coverage rate, second output value, etc. Driving mechanism: urbanization rate, industrial structure, education expenditure, etc. | Urban Statistical Yearbook, Water Resources Bulletin, Environmental Quality Report, China Environmental Statistics Yearbook, China Energy Statistical Yearbook, Bulletin on National Economic and Social Development (https://data.stats.gov.cn/; https://navi.cnki.net/knavi/yearbooks/index) |
Topographic relief | Average urban elevation | Geospatial data cloud (http://www.gscloud.cn/sources/index?pid=302&ptitle=DEM%20%E6%95%B0%E5%AD%97%E9%AB%98%E7%A8%8B%E6%95%B0%E6%8D%AE&rootid=1) DEM digital elevation data, with a resolution of 30 m |
Residential activity data | Population density and resident activity intensity | 1. Fifth and sixth census data (https://navi.cnki.net/knavi/yearbooks/index) 2. Night light value (https://www.ngdc.noaa.gov/eog/dmsp.html) 3. Urban Statistical Yearbook |
Comprehensive Evaluation | Data Fluctuation | Correlation between Data | Figure Size |
---|---|---|---|
AHP | No | No | Yes |
CRITIC | Yes | Yes | No |
Target Layer | Criterion Layer | Index Level | Criterion Attribute |
---|---|---|---|
Natural environment | Meteorologic condition | Annual average temperature (°C) | * |
Average annual relative humidity (%) | * | ||
Average annual rainfall (mm) | + | ||
Mean wind speed (m/s) | * | ||
Air pollutants | AQI | − | |
PM2.5 (μg/m3) | − | ||
PM10 (μg/m3) | − | ||
NO2 (μg/m3) | − | ||
SO2 (μg/m3) | − | ||
CO (μg/m3) | − | ||
O3 (μg/m3) | − | ||
Cultural environment | Air control | Green and cover rate in the built-up area (%) | + |
Per capita park green area (m2) | + | ||
Number of days with good or above Grade 2 air quality (days) | + | ||
Industrial smoke (powder) dust treatment rate (%) | + | ||
Economic development | Highway passenger Volume (10,000 persons) | − | |
Highway freight volume (10,000 tons) | − | ||
Number of operating vehicles (vehicles) with bus (electric) vehicles | + | ||
Construction of urban housing (10,000 square meters) | − | ||
The second output value accounted for the GDP proportion (%) | − | ||
Urban population density (people/km2) | − | ||
Energy consumption | Total gas supply (artificial and natural gas) (ten thousand cubic meters) | − | |
Total LPG gas supply (ton) | − | ||
Dust industrial dust emission per capita (ton) | − | ||
Per capita industrial sulfur dioxide emissions (ton) | − |
Index Level | 2013 Comprehensive Weight | 2014 Comprehensive Weight | 2015 Comprehensive Weight | 2016 Comprehensive Weight | 2017 Comprehensive Weight | 2018 Comprehensive Weight |
---|---|---|---|---|---|---|
Annual average temperature (°C) | 0.019186 | 0.015905 | 0.015588 | 0.016971 | 0.015306 | 0.017485 |
Average annual relative humidity (%) | 0.028799 | 0.024098 | 0.023433 | 0.025708 | 0.024022 | 0.026126 |
Average annual rainfall (mm) | 0.061549 | 0.061281 | 0.057241 | 0.065213 | 0.054655 | 0.022764 |
Mean wind speed (m/s) | 0.048002 | 0.049271 | 0.049399 | 0.049417 | 0.046642 | 0.049372 |
AQI | 0.042333 | 0.055785 | 0.066665 | 0.075073 | 0.069676 | 0.062174 |
PM2.5 (μg/m3) | 0.054970 | 0.042038 | 0.047260 | 0.051424 | 0.050467 | 0.048745 |
PM10 (μg/m3) | 0.046133 | 0.038082 | 0.048433 | 0.053159 | 0.047379 | 0.050701 |
NO2 (μg/m3) | 0.016445 | 0.012690 | 0.017277 | 0.016062 | 0.012890 | 0.015858 |
SO2 (μg/m3) | 0.017858 | 0.015636 | 0.017651 | 0.019439 | 0.018522 | 0.017570 |
CO (μg/m3) | 0.015268 | 0.016656 | 0.013529 | 0.016141 | 0.015839 | 0.014444 |
O3 (μg/m3) | 0.029677 | 0.025952 | 0.033510 | 0.034874 | 0.033105 | 0.019983 |
Green and cover rate in the built-up area (%) | 0.038226 | 0.030201 | 0.040336 | 0.037270 | 0.028553 | 0.024926 |
Per capita park green area (m2) | 0.026577 | 0.026536 | 0.024790 | 0.024517 | 0.028778 | 0.032346 |
Number of days with good or above Grade 2 air quality (days) | 0.125913 | 0.157345 | 0.178139 | 0.126340 | 0.175582 | 0.203186 |
Industrial smoke (powder) dust treatment rate (%) | 0.119979 | 0.118696 | 0.108977 | 0.110473 | 0.114188 | 0.118164 |
Highway passenger Volume (10,000 persons) | 0.014979 | 0.016717 | 0.012565 | 0.013426 | 0.015028 | 0.013418 |
Highway freight volume (10,000 tons) | 0.010766 | 0.016967 | 0.009485 | 0.010096 | 0.016781 | 0.015835 |
Number of operating vehicles (vehicles) with bus (electric) vehicles | 0.011740 | 0.011242 | 0.013353 | 0.012783 | 0.011584 | 0.012609 |
Construction of urban housing (10,000 square meters) | 0.022987 | 0.021975 | 0.015328 | 0.015610 | 0.024970 | 0.016155 |
The second output value accounted for the GDP proportion (%) | 0.076449 | 0.083266 | 0.074329 | 0.078510 | 0.054512 | 0.075008 |
Urban population density (people/km2) | 0.102396 | 0.092155 | 0.076169 | 0.075524 | 0.086972 | 0.077324 |
Total gas supply (artificial and natural gas) (ten thousand cubic meters) | 0.006516 | 0.006760 | 0.006070 | 0.006361 | 0.006031 | 0.006240 |
Total LPG gas supply (ton) | 0.008423 | 0.008166 | 0.009590 | 0.009891 | 0.008388 | 0.011817 |
Dust industrial dust emission per capita (ton) | 0.017259 | 0.021747 | 0.021214 | 0.024329 | 0.018918 | 0.020891 |
Per capita industrial sulfur dioxide emissions (ton) | 0.037569 | 0.030831 | 0.019666 | 0.031389 | 0.021214 | 0.026860 |
Year | Moran’s I | E(I) | p-Value | z-Value |
---|---|---|---|---|
2013 | 0.3750 | −0.0035 | 0.0010 | 9.6651 |
2014 | 0.5994 | −0.0035 | 0.0010 | 15.0378 |
2015 | 0.7345 | −0.0035 | 0.0010 | 19.8175 |
2016 | 0.7190 | −0.0035 | 0.0010 | 18.2767 |
2017 | 0.7090 | −0.0041 | 0.0010 | 18.5267 |
2018 | 0.6710 | −0.0044 | 0.0010 | 16.2060 |
Variables | Fixed Effect | Random Effect | ||||
---|---|---|---|---|---|---|
Coefficient | t-Stat | Probability | Coefficient | t-Stat | Probability | |
lnX1 | 0.001165 | 1.223659 | 0.221081 | 3145.404 | −0.243600 | 0.807541 |
lnX2 | 0 | −0.143361 | 0.886005 | −0.000215 | 0.197614 | 0.843347 |
lnX3 | −0.000059 | −0.994290 | 0.320082 | 0 | 1.180926 | 0.237632 |
lnX4 | −0.000031 | −5.252817 | 0 | 0.000046 | −1.138767 | 0.2548 |
lnX5 | −0.015320 | −7.157567 | 0 | −0.000009 | −1.037222 | 0.299633 |
lnX6 | 0 | 0.01399 | 0.988838 | −0.002590 | 3.320827 | 0.000898 |
lnX7 | 0.001305 | 1.725711 | 0.084399 | 0.000013 | 1.781365 | 0.074853 |
W*dep.var. | 0.998985 | 3945.387 | 0 | 0.002114 | 304.5005 | 0 |
teta | - | - | - | 0.265423 | 17.24327 | 0 |
R2 | 0.6728 | 0.8886 | ||||
Sigma2 | 0.0025 | 0.0009 | ||||
log-likelihood | 2585.6269 | 3145.4038 | ||||
LMlag | 1781.3148 | 40,034.6297 | ||||
R-LMlag | 8029.8153 | 71,8143.7069 | ||||
LMerror | 71.323 | 4.0723 | ||||
R-LMerror | 6319.8235 | 678,113.1495 |
Variables | Fixed Effect | Random Effect | ||||
---|---|---|---|---|---|---|
Coefficient | t-Stat | Probability | Coefficient | t-Stat | Probability | |
lnX1 | −0.002480 | −2.334380 | 0.019576 | −0.001050 | −1.078920 | 0.280624 |
lnX2 | 0 | −0.375060 | 0.707617 | 0 | −0.190200 | 0.849149 |
lnX3 | −0.000016 | −0.269210 | 0.787766 | 0.000049 | 1.173063 | 0.24077 |
lnX4 | −0.000020 | −3.519320 | 0.000433 | −0.000003 | −0.312250 | 0.75485 |
lnX5 | −0.017970 | −8.161420 | 0 | −0.002380 | −0.417340 | 0.676432 |
lnX6 | 0.000011 | 2.862237 | 0.004207 | 0.00029 | 16.3351 | 0 |
lnX7 | −0.002130 | −2.390450 | 0.016828 | 0.00605 | 2.044096 | 0.040944 |
spat.aut. | 0.988972 | 334.0992 | 0 | 0.996368 | 23658.92 | 0 |
teta | - | - | - | 88.19199 | 13.41322 | 0 |
R2 | −0.011800 | 0.8881 | ||||
Sigma2 | 0.0025 | 0.0009 | ||||
log-likelihood | 2604.1892 | 2620.315 | ||||
LMlag | 3702.4477 | 1191.641 | ||||
R-LMlag | 347.4313 | 1250.0513 | ||||
LMerror | 4933.6623 | 0.0002 | ||||
R-LMerror | 1578.6458 | 58.4105 |
Variables | Fixed Effect | Random Effect | ||||
---|---|---|---|---|---|---|
Coefficient | t-Stat | Probability | Coefficient | t-Stat | Probability | |
lnX1 | −0.002466 | −2.361442 | 0.018204 | −0.001563 | −1.698671 | 0.089381 |
lnX2 | 0.000000 | 0.420666 | 0.673999 | 0.000000 | 0.016323 | 0.986977 |
lnX3 | −0.000029 | −0.496513 | 0.619532 | −0.000008 | −0.181877 | 0.855680 |
lnX4 | −0.000024 | −4.116232 | 0.000039 | −0.000013 | −1.636228 | 0.101792 |
lnX5 | −0.020358 | −9.184800 | 0.000000 | −0.017493 | −4.818972 | 0.000001 |
lnX6 | 0.000028 | 5.333164 | 0.000000 | 0.000017 | 1.615204 | 0.106266 |
lnX7 | −0.001899 | −2.066503 | 0.038781 | −0.002697 | −1.738743 | 0.082080 |
W*lnX1 | 0.013040 | 4.546095 | 0.000005 | 0.002786 | 0.983582 | 0.325321 |
W*lnX2 | 0.000000 | 2.654114 | 0.007952 | 0.000000 | 2.240181 | 0.025079 |
W*lnX3 | −0.000462 | −2.641054 | 0.008265 | −0.000162 | −1.505127 | 0.132291 |
W*lnX4 | −0.000078 | −2.628386 | 0.008579 | 0.000055 | 1.822676 | 0.068352 |
W*lnX5 | −0.016386 | −2.115604 | 0.034379 | 0.027305 | 4.767539 | 0.000002 |
W*lnX6 | −0.000046 | −4.802428 | 0.000002 | −0.000016 | −0.927091 | 0.353879 |
W*lnX7 | 0.013126 | 5.716435 | 0.000000 | 0.008057 | 2.475201 | 0.013316 |
W*dep.var. | 0.993996 | 657.950793 | 0.000000 | 0.967977 | 129.876552 | 0.000000 |
teta | 0.285798 | 17.304632 | 0.000000 | |||
R2 | 0.699600 | 0.888400 | ||||
Sigma2 | 0.002300 | 0.000900 | ||||
log-likelihood | 2650.950500 | 3164.152300 | ||||
Wald_spatial_lag | 157.679600 | 57.688700 | ||||
Wald_spatial_error | 107.286800 | 31.766700 | ||||
LR_spatial_ | 130.124000 | 888.309000 | ||||
lag | ||||||
LR_spatial_error | 83.633500 | 1938.500000 | ||||
Hanuman | 58.286900 |
Effect Evaluation | Variables | Fixed Effect | Random Effect | ||||
---|---|---|---|---|---|---|---|
Coefficient | t-Stat | t-Prob | Coefficient | t-Stat | t-Prob | ||
Direct effects | lnX1 | 0.000388 | 0.306805 | 0.759217 | −0.001340 | −1.173813 | 0.241453 |
lnX2 | 0.000000 | 2.085903 | 0.037880 | 0.000000 | 1.194471 | 0.233290 | |
lnX3 | −0.000163 | −2.135358 | 0.033587 | −0.000040 | −0.796255 | 0.426549 | |
lnX4 | −0.000051 | −4.325309 | 0.000021 | −0.000005 | −0.481062 | 0.630843 | |
lnX5 | −0.030116 | −9.598687 | 0.000000 | −0.015463 | −4.234122 | 0.000031 | |
lnX6 | 0.000023 | 4.938440 | 0.000001 | 0.000017 | 1.842039 | 0.066512 | |
lnX7 | 0.001129 | 1.074293 | 0.283603 | −0.001550 | −1.000729 | 0.317810 | |
Indirect effects | lnX1 | 0.800067 | 3.862730 | 0.000139 | 0.041324 | 0.450830 | 0.652456 |
lnX2 | 0.000007 | 2.622878 | 0.009190 | 0.000001 | 1.924636 | 0.055274 | |
lnX3 | −0.037285 | −2.762278 | 0.006114 | −0.005359 | −1.290572 | 0.197902 | |
lnX4 | −0.007724 | −3.092455 | 0.002183 | 0.001243 | 1.326532 | 0.185729 | |
lnX5 | −2.779643 | −4.522011 | 0.000009 | 0.311432 | 2.093364 | 0.037204 | |
lnX6 | −0.001384 | −3.365275 | 0.000870 | −0.000007 | −0.024911 | 0.980143 | |
lnX7 | 0.861756 | 5.040209 | 0.000001 | 0.170666 | 1.906720 | 0.057566 | |
Total effects | lnX1 | 0.800455 | 3.851552 | 0.000145 | 0.039984 | 0.433206 | 0.665194 |
lnX2 | 0.000007 | 2.622373 | 0.009203 | 0.000001 | 1.922912 | 0.055491 | |
lnX3 | −0.037448 | −2.764135 | 0.006081 | −0.005399 | −1.290961 | 0.197767 | |
lnX4 | −0.007774 | −3.099903 | 0.002130 | 0.001237 | 1.310436 | 0.191107 | |
lnX5 | −2.809759 | −4.554184 | 0.000008 | 0.295968 | 1.977671 | 0.048932 | |
lnX6 | −0.001361 | −3.325550 | 0.000998 | 0.000009 | 0.030815 | 0.975439 | |
lnX7 | 0.862885 | 5.032566 | 0.000001 | 0.169116 | 1.882057 | 0.060851 |
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Li, X.; Li, S.; Tian, S.; Guan, Y.; Liu, H. Air Quality and the Spatial-Temporal Differentiation of Mechanisms Underlying Chinese Urban Human Settlements. Land 2021, 10, 1207. https://doi.org/10.3390/land10111207
Li X, Li S, Tian S, Guan Y, Liu H. Air Quality and the Spatial-Temporal Differentiation of Mechanisms Underlying Chinese Urban Human Settlements. Land. 2021; 10(11):1207. https://doi.org/10.3390/land10111207
Chicago/Turabian StyleLi, Xueming, Songbo Li, Shenzhen Tian, Yingying Guan, and He Liu. 2021. "Air Quality and the Spatial-Temporal Differentiation of Mechanisms Underlying Chinese Urban Human Settlements" Land 10, no. 11: 1207. https://doi.org/10.3390/land10111207
APA StyleLi, X., Li, S., Tian, S., Guan, Y., & Liu, H. (2021). Air Quality and the Spatial-Temporal Differentiation of Mechanisms Underlying Chinese Urban Human Settlements. Land, 10(11), 1207. https://doi.org/10.3390/land10111207