Datasets:

electricsheepafrica
/

africa-synth-climate-air-quality-urban-africa-all

record_id stringlengths 9 9	country stringclasses 15 values	city stringclasses 54 values	year int64 2.02k 2.03k	month int64 1 12	urban_population int64 100k 25M	population_density_km2 int64 500 25k	vehicle_fleet_size int64 10.1k 6.94M	vehicle_density_per_1000 float64 100 300	industrial_facilities int64 5 500	household_biomass_use_pct float64 5 95	PM25_ug_m3 float64 11.1 200	PM10_ug_m3 float64 15 400	NO2_ug_m3 float64 44.7 180	O3_ppb float64 20 120	CO_mg_m3 float64 1.12 15	SO2_ug_m3 float64 2 100	source_traffic_pct float64 12.2 85.2	source_industrial_pct float64 0.9 71.1	source_household_pct float64 2.2 57.5	source_dust_pct float64 2.1 29.5	PM25_who_exceedance_factor float64 2.22 40	aqi_us int64 102 500	aqi_category stringclasses 4 values	respiratory_risk_index float64 1.47 10	cardiovascular_risk_index float64 0.56 10	estimated_premature_deaths_yearly int64 10 15k	economic_cost_health_million_usd float64 0.5 2.24k	monitoring_coverage_pct float64 5 80	air_quality_policy_strength float64 0.03 4.87
AQ_000000	Niger	Niamey	2,023	5	8,724,324	2,876	1,131,480	129.7	500	95	200	372.7	180	120	14.7	100	15.1	58.2	22.1	4.6	40	500	Hazardous	10	10	15,000	1,866.59	54.2	0.48
AQ_000001	Uganda	Jinja	2,023	12	2,101,155	500	252,717	120.3	132	84.3	95.9	199.8	180	68.9	6.13	87.7	26.4	29	37	7.7	19.18	412	Hazardous	10	7.12	1,209	75.66	7	1.98
AQ_000002	DRC	Lubumbashi	2,018	8	6,811,669	3,171	1,352,668	198.6	279	90.6	126.7	198.4	180	120	6.53	100	28	39.3	25.5	7.2	25.34	500	Hazardous	10	9.63	3,342	399.4	38.4	3.92
AQ_000003	Ghana	Accra	2,025	3	7,086,215	13,072	1,901,415	268.3	368	58.8	200	400	180	120	15	100	34	46.7	14.9	4.4	40	500	Hazardous	10	10	7,371	829.2	36.4	3.25
AQ_000004	Rwanda	Gisenyi	2,024	3	2,873,876	500	643,705	224	143	95	166.4	400	180	85.8	15	77.6	37.2	23.8	31.6	7.5	33.28	500	Hazardous	10	10	4,546	595.38	40.7	3.92
AQ_000005	Malawi	Mzuzu	2,021	4	1,005,774	500	233,996	232.7	54	95	83.4	180.1	180	51.2	11.45	20.7	44	10.2	35.9	9.9	16.68	359	Hazardous	10	8.05	686	40.97	13	0.79
AQ_000006	Uganda	Gulu	2,024	6	602,993	2,356	89,206	147.9	36	95	60.6	88.6	180	96.4	11.05	33.3	35.6	8.7	45.7	10.1	12.12	357	Hazardous	10	6.64	338	18.89	57.6	2.32
AQ_000007	South Africa	Pretoria	2,020	2	1,522,562	4,713	237,616	156.1	80	95	75.2	178.8	180	59	15	61.9	33.6	17.2	41	8.2	15.04	348	Hazardous	9	9.07	800	111.32	19.6	1.55
AQ_000008	Senegal	Dakar	2,018	5	299,591	2,307	30,162	100.7	12	48.8	41.6	87.3	126.3	35.4	6.55	11.1	37.5	4.5	36.3	21.7	8.32	209	Very_Unhealthy	5.7	4.15	87	4.76	18.2	0.93
AQ_000009	Nigeria	Abuja	2,024	7	3,111,992	500	480,256	154.3	174	64.8	90.1	182.7	180	84.7	6.01	71.2	31.4	35.5	26.4	6.7	18.02	416	Hazardous	9.52	6.81	1,294	82.67	70.8	1.8
AQ_000010	South Africa	Johannesburg	2,022	12	4,820,498	500	891,213	184.9	250	42.7	149.3	309.4	180	84.8	7.43	100	32.5	43.9	15	8.6	29.86	500	Hazardous	10	8.14	4,955	515.82	28.6	2.84
AQ_000011	DRC	Mbuji-Mayi	2,024	6	3,681,709	1,685	389,657	105.8	209	86.5	107.9	153.6	175.3	116	7.55	98.5	19.3	38.2	31.6	10.9	21.58	496	Hazardous	10	6.73	1,970	141.7	28.3	3.15
AQ_000012	Kenya	Kisumu	2,025	7	1,437,642	500	206,972	144	51	54.8	50.8	79	138.3	61.3	8.84	36	43.3	15.3	33	8.4	10.16	269	Very_Unhealthy	7.17	5.67	405	57.6	47.3	2.06
AQ_000013	Nigeria	Port Harcourt	2,021	11	5,157,865	9,148	802,327	155.6	340	95	200	400	180	43.5	11.45	100	22	48	26.8	3.1	40	500	Hazardous	10	10	5,085	287.09	58.3	2.58
AQ_000014	Uganda	Gulu	2,024	5	3,696,256	4,240	826,515	223.6	147	95	125.5	240.8	180	82.5	15	63.3	37.4	24.6	31.8	6.2	25.1	500	Hazardous	10	10	3,919	575.58	31.4	0.36
AQ_000015	Ethiopia	Dire Dawa	2,019	5	1,461,556	3,718	427,057	292.2	80	68.7	101.7	199.2	180	64.1	10.46	67.2	52.2	14.3	24.6	8.9	20.34	421	Hazardous	10	9.22	1,163	85.11	37.1	3.59
AQ_000016	Malawi	Mzuzu	2,024	2	1,490,751	1,148	384,357	257.8	89	76.9	116.3	211.1	180	88	7.55	52.8	48.7	16.8	29	5.5	23.26	486	Hazardous	10	9.31	1,464	146.66	35.3	3.4
AQ_000017	Niger	Zinder	2,022	8	3,148,600	5,260	637,591	202.5	147	51.1	103.8	190.3	180	87.8	10.17	99.6	41.3	30	20.9	7.8	20.76	454	Hazardous	10	8.38	1,638	154.09	61.3	0.55
AQ_000018	Mali	Sikasso	2,019	3	2,255,376	823	239,607	106.2	115	80.5	64.5	100.5	113.8	54.2	11	67.4	24.9	27	37.8	10.3	12.9	283	Very_Unhealthy	8	5.37	1,022	146.53	69.4	3
AQ_000019	Rwanda	Kigali	2,022	10	2,386,649	4,954	499,396	209.2	143	59.6	127.3	312.7	180	70.6	8.79	100	41.3	28.2	23.5	6.9	25.46	492	Hazardous	10	10	1,229	106.99	5	2.7
AQ_000020	Malawi	Lilongwe	2,024	7	3,554,554	3,801	972,965	273.7	174	90.4	101.1	128.4	180	120	12.38	90.4	41.7	26.5	27.5	4.3	20.22	486	Hazardous	10	10	2,563	353.56	13.1	0.66
AQ_000021	South Africa	Cape Town	2,018	6	11,195,965	9,883	3,096,886	276.6	481	57.8	200	285.9	180	120	15	100	30.9	53.7	12.9	2.5	40	500	Hazardous	10	10	11,600	1,266.06	9.1	0.32
AQ_000022	Ethiopia	Addis Ababa	2,019	11	4,452,893	8,757	694,019	155.9	160	59.5	103.4	256.4	180	80.3	8.71	62.6	31.6	32.4	24.1	11.8	20.68	444	Hazardous	10	7.36	2,367	243.61	23.2	0.75
AQ_000023	Malawi	Blantyre	2,020	8	757,259	500	166,888	220.4	33	95	47.1	76	180	57.6	14.57	30.7	45.7	6.8	39.4	8	9.42	276	Very_Unhealthy	9	5.35	124	13.66	9.7	0.78
AQ_000024	Malawi	Mzuzu	2,023	6	4,860,099	697	852,338	175.4	267	69.8	78.7	117	180	109	9.95	95.4	29	44.2	23.1	3.7	15.74	417	Hazardous	10	5.81	2,742	302.33	7.7	2.25
AQ_000025	South Africa	Pretoria	2,018	9	537,370	515	72,394	134.7	31	68.6	46.4	75	160.6	63.7	11.83	14.4	38.8	8.9	39.5	12.8	9.28	272	Very_Unhealthy	8.84	6.36	150	19.04	32.7	2.7
AQ_000026	South Africa	Cape Town	2,020	2	10,984,109	717	1,304,298	118.7	500	75.3	200	400	180	97.6	11.42	100	14.5	61.3	18.5	5.7	40	500	Hazardous	10	10	15,000	2,084.94	33.5	1.98
AQ_000027	Mozambique	Beira	2,018	6	2,752,513	9,217	602,808	219	134	87.1	90.3	144.9	180	118.6	7.95	71.9	39.8	24.4	31.7	4.2	18.06	458	Hazardous	10	5.93	1,209	120.31	18.1	0.68
AQ_000028	Senegal	Saint-Louis	2,022	10	2,012,262	500	461,832	229.5	98	95	151.3	387.1	180	80.8	15	77	39.8	17	33	10.1	30.26	500	Hazardous	10	10	1,840	248.16	66.8	1.9
AQ_000029	Niger	Zinder	2,019	10	2,900,883	949	314,381	108.4	191	55.1	126.7	195.9	141.2	89.1	5.79	100	23.8	41.9	24.2	10.1	25.34	494	Hazardous	10	9.72	2,628	164.76	46	1.84
AQ_000030	Malawi	Blantyre	2,025	8	6,735,789	500	1,623,791	241.1	410	83.3	200	263.3	180	120	6.86	100	27.5	46.8	19	6.8	40	500	Hazardous	10	10	5,702	404.5	19	3.24
AQ_000031	Kenya	Mombasa	2,022	11	2,985,958	2,028	857,636	287.2	125	95	115.9	260.1	180	69.6	15	61.8	44.2	19.3	29.3	7.3	23.18	463	Hazardous	10	9.79	3,484	472.13	15.5	3.5
AQ_000032	Senegal	Thiès	2,019	11	8,853,041	25,000	2,295,534	259.3	389	48.2	196.3	358.1	180	114.6	11.07	100	32.5	48.8	12.1	6.6	39.26	500	Hazardous	10	10	10,492	1,400.16	11.7	1.01
AQ_000033	Mali	Sikasso	2,018	4	117,318	500	18,499	157.7	6	71.9	39.3	73.2	154	58.7	8.95	4.8	47.3	1.8	43.1	7.8	7.86	245	Very_Unhealthy	9	4.13	40	4.85	32	1.64
AQ_000034	Uganda	Kampala	2,024	7	1,374,030	846	297,521	216.5	81	30.7	57.8	66	180	79.8	10.95	52.9	56.5	21.1	16	6.4	11.56	330	Hazardous	9.71	5.19	696	54.61	5	0.87
AQ_000035	Mozambique	Beira	2,025	10	913,319	2,741	112,689	123.4	48	63.6	51.5	93.2	180	40.6	9.19	18.3	35	13.6	36.1	15.3	10.3	267	Very_Unhealthy	7.95	4.47	410	41.03	36.8	0.52
AQ_000036	Senegal	Saint-Louis	2,018	10	8,909,645	5,757	1,981,753	222.4	383	89	173.3	376.5	180	89.9	8.27	100	26.8	46.2	21.5	5.4	34.66	500	Hazardous	10	10	14,231	1,110.93	52.4	4.05
AQ_000037	Zambia	Ndola	2,023	6	2,148,739	500	627,904	292.2	98	45.8	80.5	90.6	180	102.4	8.4	86.2	56.4	18.9	17.7	7.1	16.1	414	Hazardous	10	6.24	730	63.82	43.8	0.65
AQ_000038	DRC	Kinshasa	2,025	10	273,972	500	74,592	272.3	14	82.2	77.1	141.9	180	57.6	7.35	13.3	57.3	2.9	34.6	5.1	15.42	351	Hazardous	10	6.87	98	14.26	34.9	1.28
AQ_000039	Nigeria	Kano	2,024	8	4,606,164	2,083	784,953	170.4	253	51.7	162.8	331.3	180	120	6.5	100	30.2	44.8	18.3	6.8	32.56	500	Hazardous	10	9.65	4,472	441.39	37	3.78
AQ_000040	South Africa	Durban	2,023	5	1,107,890	3,479	199,367	180	63	95	74.1	157.6	180	60.7	6.85	39.5	36.7	12.9	38.8	11.6	14.82	348	Hazardous	10	6.81	293	22.23	31.2	1.85
AQ_000041	Nigeria	Lagos	2,018	8	713,575	500	177,179	248.3	40	83.3	87.6	187.3	180	73.6	12.27	15.6	50.8	8.2	34.1	6.9	17.52	397	Hazardous	9.63	9.27	472	64.22	35.2	1.38
AQ_000042	Mali	Mopti	2,019	1	3,743,504	1,354	829,543	221.6	168	77.6	200	400	180	61.5	14.88	100	36.8	27.9	25.8	9.6	40	500	Hazardous	10	10	4,304	616.57	19.4	3.53
AQ_000043	DRC	Lubumbashi	2,022	2	3,676,819	16,656	1,098,494	298.8	194	92.4	154	359.8	180	106.5	15	100	40.6	26.3	25.1	8	30.8	500	Hazardous	10	10	4,975	619.86	5	2.86
AQ_000044	Rwanda	Butare	2,021	5	980,992	1,928	166,879	170.1	53	70	89	179.8	180	58.7	12.39	43.1	44	13.7	36.2	6	17.8	382	Hazardous	9.17	9.08	331	35.51	27.7	2.06
AQ_000045	Uganda	Entebbe	2,023	1	744,387	2,735	182,439	245.1	34	79.2	69.6	156.1	180	47.9	11.56	25.7	52.3	7.3	33.8	6.6	13.92	321	Hazardous	8.58	7.45	366	23.19	48.6	2.93
AQ_000046	Niger	Maradi	2,018	9	1,869,258	1,509	459,037	245.6	84	86.1	107.6	235.9	180	84.7	15	71.7	44.9	15.4	31.5	8.2	21.52	460	Hazardous	10	10	1,419	120.86	54.4	3.14
AQ_000047	Senegal	Thiès	2,018	4	7,749,460	3,172	887,297	114.5	395	95	181.1	400	180	110.9	12.69	100	15.4	53	25.5	6.2	36.22	500	Hazardous	10	10	14,124	1,443.93	24.5	3.2
AQ_000048	South Africa	Durban	2,023	5	1,393,099	1,428	219,691	157.7	59	95	88	174.3	180	58.8	15	54.4	34.2	12.8	41.2	11.7	17.6	380	Hazardous	10	9.35	881	45.67	18.1	1.21
AQ_000049	Ghana	Accra	2,023	1	1,544,549	500	243,827	157.9	64	95	145.2	400	180	48.6	12.42	29.5	34.8	14.1	41.8	9.3	29.04	500	Hazardous	10	10	1,678	102.47	61.3	0.69
AQ_000050	Ghana	Tamale	2,018	2	402,393	1,328	106,635	265	16	80.6	200	400	180	44.6	11.09	7.3	54.3	3.3	33	9.4	40	500	Hazardous	10	10	416	52.9	34.7	0.72
AQ_000051	Tanzania	Arusha	2,025	11	2,668,374	1,785	494,714	185.4	144	95	93.6	204.1	180	56.3	10.1	80.1	34.3	26.6	35.2	3.9	18.72	391	Hazardous	10	8.92	2,159	301.11	6.3	2.23
AQ_000052	DRC	Lubumbashi	2,019	2	847,667	1,481	238,785	281.7	39	95	100.1	233	180	76	7.08	19.9	52.2	7.2	35.2	5.4	20.02	431	Hazardous	10	7.45	751	104.04	54.3	2.04
AQ_000053	South Africa	Durban	2,024	3	180,053	500	41,290	229.3	6	70	72.3	131.1	180	46	5.64	3.3	52.9	1.4	32.3	13.4	14.46	325	Hazardous	10	5.94	124	14.26	19.4	1.98
AQ_000054	South Africa	Pretoria	2,023	10	7,525,653	21,869	1,594,581	211.9	444	72.4	200	400	180	97.7	9.43	100	25	52.4	17.1	5.6	40	500	Hazardous	10	10	8,927	1,197.36	38.1	2.67
AQ_000055	Tanzania	Mwanza	2,022	11	9,344,062	500	1,148,829	122.9	439	95	200	400	180	93.7	10.46	100	15.8	56.4	24.4	3.3	40	500	Hazardous	10	10	15,000	1,746.42	12	4.05
AQ_000056	Niger	Zinder	2,019	3	1,154,282	2,997	282,907	245.1	61	23.2	90.6	173.3	180	84.7	5.09	30.6	63.6	15.8	12	8.5	18.12	418	Hazardous	10	6	720	86.93	5	4.16
AQ_000057	Niger	Maradi	2,020	11	610,744	500	77,132	126.3	30	44.4	114.9	400	142.8	30.9	6.82	23.6	41.4	9.8	29.1	19.6	22.98	395	Hazardous	8.51	8.77	353	31.93	5	1.44
AQ_000058	Tanzania	Arusha	2,021	11	600,377	2,354	129,273	215.3	26	44.6	87.5	176.7	180	51.8	12.81	16.4	57.7	7	23.9	11.5	17.5	370	Hazardous	10	8.77	480	69.28	27.3	1.56
AQ_000059	Mali	Bamako	2,025	8	3,960,282	5,782	489,648	123.6	190	80	77.9	160.1	180	79.2	13.06	91.1	23.5	36.2	30.4	9.9	15.58	379	Hazardous	9.04	6.36	2,439	228.64	46.9	1.22
AQ_000060	Rwanda	Gisenyi	2,023	5	3,031,425	657	826,966	272.8	151	71.1	118.6	249.8	180	65.8	11.41	100	44.1	24.4	23	8.4	23.72	465	Hazardous	10	10	3,289	228.51	47.4	3.96
AQ_000061	Tanzania	Dar es Salaam	2,020	2	2,796,171	2,671	297,175	106.3	164	35.7	88	168.9	180	51.8	7.25	100	26.6	41	17.8	14.6	17.6	372	Hazardous	10	5.38	2,271	254.13	41.3	1.9
AQ_000062	Ethiopia	Mekelle	2,023	8	3,917,576	1,629	800,508	204.3	199	95	91	122.6	180	113.5	14.98	100	33.3	32.4	30.9	3.4	18.2	453	Hazardous	10	10	2,272	276.15	61	1.97
AQ_000063	Nigeria	Lagos	2,024	4	963,301	546	220,875	229.3	60	80.5	85.6	163.2	180	76.6	14.71	48	47.3	12.4	33.2	7.2	17.12	395	Hazardous	10	8.38	727	66.37	33.2	2.18
AQ_000064	Ethiopia	Dire Dawa	2,019	7	7,355,391	500	1,584,152	215.4	338	75.9	176.6	229.5	180	120	10.22	100	28.5	44.8	20.1	6.5	35.32	500	Hazardous	10	10	13,036	1,755.1	13.6	3.85
AQ_000065	Mali	Sikasso	2,018	6	6,285,577	9,530	1,211,968	192.8	383	61.7	192.2	318	180	120	7.95	100	25.9	51.4	16.6	6.2	38.44	500	Hazardous	10	10	4,988	446.06	30.5	1.59
AQ_000066	Senegal	Thiès	2,019	12	835,387	1,262	241,289	288.8	26	66	74.3	188.8	180	54.6	10.6	18.5	60.8	5.5	27.8	6	14.86	341	Hazardous	10	7.96	582	35.49	45.8	4.21
AQ_000067	Malawi	Mzuzu	2,024	5	2,729,176	3,467	641,454	235	141	5	110.3	259.7	180	68.6	8.11	94.2	53.9	32.3	2.3	11.5	22.06	448	Hazardous	10	9.43	2,803	242.68	71.2	2.07
AQ_000068	DRC	Lubumbashi	2,024	8	2,592,560	1,095	298,913	115.3	137	95	66.2	108.4	180	70.7	6.01	93.8	23.3	27.7	38.4	10.6	13.24	340	Hazardous	10	6.1	884	72.69	13.6	1.39
AQ_000069	Tanzania	Dodoma	2,025	12	3,385,918	500	401,721	118.6	159	82.1	132.1	238.9	168.2	86.6	4.75	97.2	25.3	33.9	35	5.8	26.42	500	Hazardous	10	8.22	3,297	166.09	51.7	3.03
AQ_000070	Nigeria	Abuja	2,019	2	355,983	753	101,766	285.9	17	86.4	184.3	400	180	58.3	15	10.2	56.8	3.4	34.3	5.5	36.86	500	Hazardous	10	10	553	61.42	50.4	0.97
AQ_000071	Ghana	Tamale	2,025	1	5,348,226	8,793	1,596,701	298.5	288	95	200	400	180	46.8	12.35	100	37.1	35.8	23.6	3.6	40	500	Hazardous	10	10	4,772	481.83	61.6	1.79
AQ_000072	Malawi	Blantyre	2,025	5	1,900,130	2,840	351,340	184.9	110	95	100.1	241.3	180	53.6	11.35	89.3	34.6	20.6	35.6	9.3	20.02	404	Hazardous	9.56	6.86	1,341	120.68	80	2.84
AQ_000073	South Africa	Durban	2,018	11	118,481	500	15,528	131.1	5	77.4	47.7	107.9	169.7	53.2	6.15	4.1	37.8	1.4	44.6	16.2	9.54	267	Very_Unhealthy	9.06	4.06	24	2.55	45.1	1.03
AQ_000074	Senegal	Saint-Louis	2,020	9	1,157,600	3,341	318,085	274.8	67	95	66.1	108.3	180	92.9	14.71	31.7	47.9	11.7	33.1	7.4	13.22	366	Hazardous	9.67	7.79	505	41.38	19.3	1.14
AQ_000075	Rwanda	Kigali	2,025	3	7,145,012	9,942	1,348,424	188.7	378	54.7	200	372.5	180	91.9	10.85	100	27	54	15.6	3.4	40	500	Hazardous	10	10	9,833	1,340.04	5.6	1.82
AQ_000076	DRC	Lubumbashi	2,025	7	1,751,380	2,152	193,089	110.2	96	92.1	56.4	88.1	112.4	67.4	12.94	83.4	25.4	22.1	42.4	10	11.28	278	Very_Unhealthy	6.48	6.37	449	43.16	34.8	2.5
AQ_000077	Zambia	Lusaka	2,022	8	2,071,363	500	380,187	183.5	97	65.5	76	119	180	93.1	9.92	64.5	42	22.2	30	5.7	15.2	391	Hazardous	10	6.35	730	94.64	44.4	0.75
AQ_000078	Senegal	Thiès	2,024	3	5,855,237	6,489	1,680,139	286.9	310	61.5	200	400	180	119.8	15	100	38.7	41.8	16.6	3	40	500	Hazardous	10	10	4,043	383.52	46.5	1.02
AQ_000079	Uganda	Jinja	2,021	9	1,109,039	500	192,155	173.3	46	68.7	44.4	68.8	174.4	58.2	6.78	26.2	42.2	11.2	33.4	13.2	8.88	268	Very_Unhealthy	8.54	4.2	180	25.15	35.3	0.33
AQ_000080	Tanzania	Dar es Salaam	2,025	3	8,578,688	923	1,778,734	207.3	446	70.9	200	400	180	94.7	15	100	25.3	54.5	17.3	2.8	40	500	Hazardous	10	10	11,470	1,597.68	19.9	2.45
AQ_000081	Mozambique	Maputo	2,023	1	3,931,706	1,838	1,105,805	281.3	157	71	197.3	375.9	180	115.4	9.38	100	46.4	25.9	23.4	4.3	39.46	500	Hazardous	10	10	5,843	384.76	48.3	2.09
AQ_000082	Mali	Mopti	2,025	12	2,784,691	1,382	601,278	215.9	105	52.7	200	400	180	55.4	12.43	59.1	47	22.9	22.9	7.2	40	500	Hazardous	10	10	5,063	695.58	5.5	1.82
AQ_000083	Tanzania	Dar es Salaam	2,023	7	738,227	500	102,676	139.1	38	60.7	39.9	45.3	144.3	61.8	6.29	36.1	43.3	11.8	37.8	7.1	7.98	246	Very_Unhealthy	6.94	3.34	262	27.41	71.3	1.77
AQ_000084	Senegal	Saint-Louis	2,024	8	1,082,781	500	206,282	190.5	55	95	50.4	76.3	180	76.7	12.49	19.9	41	11.8	40.9	6.4	10.08	308	Hazardous	8.54	6.28	225	29.88	80	3.59
AQ_000085	Mali	Bamako	2,025	1	245,398	500	46,386	189	14	70.5	188.3	400	180	44.6	5.35	10.7	46.8	3.5	34.9	14.8	37.66	500	Hazardous	10	9.42	203	26.54	45.8	1.34
AQ_000086	Kenya	Nairobi	2,020	9	3,728,600	4,583	405,263	108.7	171	30.5	56.5	67.2	159.4	75.7	3.12	100	29.3	46.1	16.4	8.1	11.3	311	Hazardous	9.6	3.82	1,033	71.8	23	1.05
AQ_000087	Mozambique	Beira	2,021	4	1,694,347	587	278,253	164.2	91	50.7	91.8	185.5	173.5	53.3	9	44.1	40.1	22.2	24.7	13	18.36	380	Hazardous	9.96	8.32	586	76.19	12.6	2
AQ_000088	Tanzania	Arusha	2,021	6	2,365,084	1,136	506,096	214	89	95	56	78.5	180	77.3	15	37.1	41.5	17.3	36.9	4.3	11.2	322	Hazardous	9.42	7.33	946	127.13	15.7	1.94
AQ_000089	Ghana	Tamale	2,022	3	1,238,593	2,098	248,192	200.4	52	67	79.7	127	167.7	72.6	8.48	51.4	46.1	12	30.8	11	15.94	370	Hazardous	10	6.05	362	50	14.9	1.5
AQ_000090	Mozambique	Nampula	2,020	11	4,468,335	500	930,705	208.3	241	95	159.9	381.6	180	90.8	10.39	100	30.8	35.6	28.1	5.5	31.98	500	Hazardous	10	10	3,879	221.22	47.9	3.3
AQ_000091	South Africa	Johannesburg	2,018	12	1,922,218	4,345	509,590	265.1	78	80	91.7	208.4	180	74.2	11.2	53.4	49.4	14.5	29.8	6.3	18.34	408	Hazardous	10	7.47	1,587	175.79	58.9	2.89
AQ_000092	Nigeria	Abuja	2,019	1	4,859,883	1,426	1,257,212	258.7	228	78.6	200	400	180	51.1	12.84	100	38.6	34	23.4	4	40	500	Hazardous	10	10	9,216	1,287.37	5	1.55
AQ_000093	Mozambique	Beira	2,022	9	4,010,138	1,542	648,689	161.8	216	88.2	75.1	117.6	180	99.8	12.4	100	27.4	36.6	29.9	6.1	15.02	397	Hazardous	10	7.63	2,963	278.98	10.3	0.78
AQ_000094	Senegal	Thiès	2,023	9	1,793,667	2,387	428,446	238.9	78	95	81.2	110.5	180	96	15	77.5	43.4	14.2	34.5	7.9	16.24	408	Hazardous	10	8.89	646	58.79	35.4	1.11
AQ_000095	Senegal	Dakar	2,019	6	11,496,499	8,870	1,223,822	106.5	500	62.6	137.2	163	180	120	3.77	100	13.6	64	16	6.4	27.44	500	Hazardous	10	8.49	6,617	948.2	32.7	2.84
AQ_000096	Zambia	Kitwe	2,019	4	743,431	2,265	176,625	237.6	44	95	119.3	310.3	180	80.5	7.37	33.9	48.2	8.9	38.5	4.3	23.86	484	Hazardous	10	9.49	527	60.45	9.1	2.08
AQ_000097	Niger	Maradi	2,022	12	477,085	1,234	107,008	224.3	26	56.2	187.8	400	180	59.3	10.5	11.2	57.5	6.7	28.8	7	37.56	500	Hazardous	10	10	870	96.11	54.5	3.14
AQ_000098	Senegal	Thiès	2,018	6	100,000	3,961	24,135	241.4	5	95	39.2	57.9	180	72.8	14.96	3.8	52	1.1	40.9	6	7.84	275	Very_Unhealthy	10	6.86	18	1.12	45.4	2.45
AQ_000099	Tanzania	Dar es Salaam	2,018	10	1,150,141	1,144	258,952	225.1	51	71.5	85.1	166	180	54.2	15	32.8	48.7	11	31	9.3	17.02	367	Hazardous	9.71	9.95	726	83.31	28.1	0.81

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Africa Synth Climate Air Quality Urban Africa All | Africa (Electric Sheep Africa metadata inventory)

Size category: 10K<n<100K - Formats: csv - Sector: climate_environment - Engineered by Electric Sheep Africa

TL;DR

This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.

What This Dataset Covers

Public datasets help analysts inspect structured evidence, build reproducible workflows, and compare patterns across domains.

Dataset context from the existing Hugging Face card: ⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference. Air Quality Urban - Africa A comprehensive synthetic dataset monitoring urban air pollution metrics, emission sources, and health impacts across major African cities. Dataset Description This dataset provides detailed records of air quality measurements across 15 Sub-Saharan African countries, focusing on urban areas. It… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-climate-air-quality-urban-africa-all.

Dataset Profile

Field	Value
Hugging Face repo	`electricsheepafrica/africa-synth-climate-air-quality-urban-africa-all`
Sector	climate_environment
Topic tags	climate, environment, synthetic-data, sub-saharan-africa, air-quality, urban-pollution, pm2.5, synthetic
Modalities	`tabular`, `text`
Formats	`csv`
Size category	`10K<n<100K`
Countries	Africa-wide or source-defined African coverage
ISO3 coverage	`not declared`
Last modified on HF	`2026-04-14 22:52:36+00:00`
Inventory snapshot	`2026-07-16T16:00:34Z`

How To Read This Dataset

Start from the repository files and the dataset viewer when available.
Treat the README context as a fast orientation layer; confirm variable definitions and units in the data files before modeling.
Use explicit country columns when present. When geography is only implied by the title or source metadata, document that assumption in downstream analysis.
Preserve missing values until you have a defensible imputation rule.

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-synth-climate-air-quality-urban-africa-all")
print(ds)

split_name = next(iter(ds))
table = ds[split_name]
print(table.features)
print(table[:3])

Convert To Pandas When Tabular

from datasets import Dataset

first_split = ds[next(iter(ds))]
if isinstance(first_split, Dataset):
    df = first_split.to_pandas()
    print(df.head())

Data Quality Notes

This card was standardized from the Electric Sheep Africa Hugging Face metadata inventory.
Exact schema, row counts, and source files should be inspected in the repository data files.
Metadata gaps from the inventory: country, upstream_publisher.
Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.

Source And Provenance

Source context: Electric Sheep Africa metadata inventory
Publisher/source attribution: Public dataset metadata
License: CC BY 4.0
Hugging Face URL: https://huggingface.co/datasets/electricsheepafrica/africa-synth-climate-air-quality-urban-africa-all
Inventory retrieved at: 2026-07-16T16:00:34Z

Suggested Analyses

Inspect schema and missingness before modeling.
Profile variables by geography, time, and subgroup columns where present.
Join with other Electric Sheep Africa datasets using explicit country, year, and indicator fields when available.
Build reproducible notebooks that cite both the original source context and the Electric Sheep Africa Hugging Face repo.

Citation

@misc{electric_sheep_africa_africa_synth_climate_air_quality_urban_africa_all_2026,
  title        = {Africa Synth Climate Air Quality Urban Africa All | Africa (Electric Sheep Africa metadata inventory)},
  author       = {Public dataset metadata},
  year         = {2026},
  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-synth-climate-air-quality-urban-africa-all},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-synth-climate-air-quality-urban-africa-all}}
}

License

Released under CC BY 4.0.

Original source rights remain with the original publisher or data provider. Electric Sheep Africa engineering standardizes discovery metadata, documentation, and usage guidance for analysis on Hugging Face.

About Electric Sheep Africa

Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.

Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: catalog/esa_metadata_inventory/master_metadata.jsonl.

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